Freshwaters are of immense importance for human well-being. Nevertheless, they are currently facing unprecedented levels of threat from habitat loss and degradation, overexploitation, invasive species and pollution. To prevent risks to aquatic ecosystems, chemical substances, like agricultural pesticides, have to pass environmental risk assessment (ERA) before entering the market. Concurrently, large-scale environmental monitoring is used for surveillance of biological and chemical conditions in freshwaters. This thesis examines statistical methods currently used in ERA. Moreover, it presents a national-scale compilation of chemical monitoring data, an analysis of drivers and dynamics of chemical pollution in streams and, provides a large-scale risk assessment by combination with results from ERA. Additionally, software tools have been developed to integrate different datasets used in ERA. The thesis starts with a brief introduction to ERA and environmental monitoring and gives an overview of the objectives of the thesis. Chapter 2 addresses experimental setups and their statistical analyses using simulations. The results show that current designs exhibit unacceptably low statistical power, that statistical methods chosen to fit the type of data provide higher power and that statistical practices in ERA need to be revised. In chapter 3 we compiled all available pesticide monitoring data from Germany. Hereby, we focused on small streams, similar to those considered in ERA and used threshold concentrations derived during ERA for a large-scale assessment of threats to freshwaters from pesticides. This compilation resulted in the most comprehensive dataset on pesticide exposure currently available for Germany. Using state-of-the-art statistical techniques, that explicitly take the limits of quantification into account, we demonstrate that 25% of small streams are at threat from pesticides. In particular neonicotinoid pesticides are responsible for these threats. These are associated with agricultural intensity and can be detected even at low levels of agricultural use. Moreover, our results indicated that current monitoring underestimates pesticide risks, because of a sampling decoupled from precipitation events. Additionally, we provide a first large-scale study of annual pesticide exposure dynamics. Chapters 4 and 5 describe software solutions to simplify and accelerate the integration of data from ERA, environmental monitoring and ecotoxicology that is indispensable for the development of landscape-level risk assessment. Overall, this thesis contributes to the emerging discipline of statistical ecotoxicology and shows that pesticides pose a large-scale threat to small streams. Environmental monitoring can provide a post-authorisation feedback to ERA. However, to protect freshwater ecosystems ERA and environmental monitoring need to be further refined and we provide software solutions to utilise existing data for this purpose.

Die Analyse ortsbezogener Strukturdaten und Merkmale ist eine der wesentlichen Voraussetzungen für die Bewertung von Standorten und Räumen. Durch die Erfassung der lokalen Ausprägung ausgewählter Standortfaktoren und unter Zuhilfenahme eines Geographischen Informationssystems werden in der vorliegenden Dissertation die Städte und Gemeinden des hessischen Rheingau-Taunus-Kreises kategorisiert und miteinander verglichen. Hierbei erfolgt eine besondere Fokussierung auf die lokal bestehende technische und soziale Infrastruktur, um vorrangig das vorhandene Gewerbe- und Wohnumfeld sowie die darüber hinaus existierenden räumlichen Disparitäten eingehend veranschaulichen und beurteilen zu können.

Pelagic oxyclines, the transition zone between oxygen rich surface waters and oxygen depleted deep waters, are a common characteristic of eutrophic lakes during summer stratification. They can have tremendous effects on the biodiversity and the ecosystem functioning of lakes and, to add insult to injury, are expected to become more frequent and more pronounced as climate warming progresses. On these grounds, this thesis endeavors to advance the understanding of formation, persistence, and consequences of pelagic oxyclines: We test, whether the formation of metalimnetic oxygen minima is intrinsically tied to a locally enhanced oxygen consuming process, investigate the relative importance of vertical physical oxygen transport and biochemical oxygen consumption for the persistence of pelagic oxyclines, and finally assess their potential consequences for whole lake cycling. To pursue these objectives, the present thesis nearly exclusively resorts to in situ measurements. Field campaigns were conducted at three lakes in Germany featuring different types of oxyclines and resolved either a short (hours to days) or a long (weeks to months) time scale. Measurements comprised temperature, current velocity, and concentrations of oxygen and reduced substances in high temporal and vertical resolution. Additionally, vertical transport was estimated by applying the eddy correlation technique within the pelagic region for the first time. The thesis revealed, that the formation of metalimnetic oxygen minima does not necessarily depend on locally enhanced oxygen depletion, but can solely result from gradients and curvatures of oxygen concentration and depletion and their relative position to each other. Physical oxygen transport was found to be relevant for oxycline persistence when it considerably postponed anoxia on a long time scale. However, its influence on oxygen dynamics was minor on short time scales, although mixing and transport were highly variable. Biochemical consumption always dominated the fate of oxygen in pelagic oxyclines. It was primarily determined by the oxidative breakdown of organic matter originating from the epilimnion, whereas in meromictic lakes, the oxidation of reduced substances dominated. Beyond that, the results of the thesis emphasize that pelagic oxyclines can be a hotspot of mineralization and, hence, short-circuit carbon and nutrient cycling in the upper part of the water column. Overall, the present thesis highlights the importance of considering physical transport as well as biochemical cycling in future studies.

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The Web contains some extremely valuable information; however, often poor quality, inaccurate, irrelevant or fraudulent information can also be found. With the increasing amount of data available, it is becoming more and more difficult to distinguish truth from speculation on the Web. One of the most, if not the most, important criterion used to evaluate data credibility is the information source, i.e., the data origin. Trust in the information source is a valuable currency users have to evaluate such data. Data popularity, recency (or the time of validity), reliability, or vagueness ascribed to the data may also help users to judge the validity and appropriateness of information sources. We call this knowledge derived from the data the provenance of the data. Provenance is an important aspect of the Web. It is essential in identifying the suitability, veracity, and reliability of information, and in deciding whether information is to be trusted, reused, or even integrated with other information sources. Therefore, models and frameworks for representing, managing, and using provenance in the realm of Semantic Web technologies and applications are critically required. This thesis highlights the benefits of the use of provenance in different Web applications and scenarios. In particular, it presents management frameworks for querying and reasoning in the Semantic Web with provenance, and presents a collection of Semantic Web tools that explore provenance information when ranking and updating caches of Web data. To begin, this thesis discusses a highly exible and generic approach to the treatment of provenance when querying RDF datasets. The approach re-uses existing RDF modeling possibilities in order to represent provenance. It extends SPARQL query processing in such a way that given a SPARQL query for data, one may request provenance without modifying it. The use of provenance within SPARQL queries helps users to understand how RDF facts arederived, i.e., it describes the data and the operations used to produce the derived facts. Turning to more expressive Semantic Web data models, an optimized algorithm for reasoning and debugging OWL ontologies with provenance is presented. Typical reasoning tasks over an expressive Description Logic (e.g., using tableau methods to perform consistency checking, instance checking, satisfiability checking, and so on) are in the worst case doubly exponential, and in practice are often likewise very expensive. With the algorithm described in this thesis, however, one can efficiently reason in OWL ontologies with provenance, i.e., provenance is efficiently combined and propagated within the reasoning process. Users can use the derived provenance information to judge the reliability of inferences and to find errors in the ontology. Next, this thesis tackles the problem of providing to Web users the right content at the right time. The challenge is to efficiently rank a stream of messages based on user preferences. Provenance is used to represent preferences, i.e., the user defines his preferences over the messages' popularity, recency, etc. This information is then aggregated to obtain a joint ranking. The aggregation problem is related to the problem of preference aggregation in Social Choice Theory. The traditional problem formulation of preference aggregation assumes a I fixed set of preference orders and a fixed set of domain elements (e.g. messages). This work, however, investigates how an aggregated preference order has to be updated when the domain is dynamic, i.e., the aggregation approach ranks messages 'on the y' as the message passes through the system. Consequently, this thesis presents computational approaches for online preference aggregation that handle the dynamic setting more efficiently than standard ones. Lastly, this thesis addresses the scenario of caching data from the Linked Open Data (LOD) cloud. Data on the LOD cloud changes frequently and applications relying on that data - by pre-fetching data from the Web and storing local copies of it in a cache - need to continually update their caches. In order to make best use of the resources (e.g., network bandwidth for fetching data, and computation time) available, it is vital to choose a good strategy to know when to fetch data from which data source. A strategy to cope with data changes is to check for provenance. Provenance information delivered by LOD sources can denote when the resource on the Web has been changed last. Linked Data applications can benefit from this piece of information since simply checking on it may help users decide which sources need to be updated. For this purpose, this work describes an investigation of the availability and reliability of provenance information in the Linked Data sources. Another strategy for capturing data changes is to exploit provenance in a time-dependent function. Such a function should measure the frequency of the changes of LOD sources. This work describes, therefore, an approach to the analysis of data dynamics, i.e., the analysis of the change behavior of Linked Data sources over time, followed by the investigation of different scheduling update strategies to keep local LOD caches up-to-date. This thesis aims to prove the importance and benefits of the use of provenance in different Web applications and scenarios. The exibility of the approaches presented, combined with their high scalability, make this thesis a possible building block for the Semantic Web proof layer cake - the layer of provenance knowledge.