Development and application of a GC system for NMHC analyses of air samples from the CARIBIC aircraft project

The subject of this thesis is the development of a Gaschromatography (GC) system for non-methane hydrocarbons (NMHCs) and measurement of samples within the project CARIBIC (Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container, www.caribic-atmospheric.com). Air samples collected at cruising altitude from the upper troposphere and lowermost stratosphere contain hydrocarbons at low levels (ppt range), which imposes substantial demands on detection limits. Full automation enabled to maintain constant conditions during the sample processing and analyses. Additionally, automation allows overnight operation thus saving time. A gas chromatography using flame ionization detection (FID) together with the dual column approach enables simultaneous detection with almost equal carbon atom response for all hydrocarbons except for ethyne. The first part of this thesis presents the technical descriptions of individual parts of the analytical system. Apart from the sample treatment and calibration procedures, the sample collector is described. The second part deals with analytical performance of the GC system by discussing tests that had been made. Finally, results for measurement flight are assessed in terms of quality of the data and two flights are discussed in detail. Analytical performance is characterized using detection limits for each compound, using uncertainties for each compound, using tests of calibration mixture conditioning and carbon dioxide trap to find out their influence on analyses, and finally by comparing the responses of calibrated substances during period when analyses of the flights were made. Comparison of both systems shows good agreement. However, because of insufficient capacity of the CO2 trap the signal of one column was suppressed due to breakthroughed carbon dioxide so much that its results appeared to be unreliable. Plausibility tests for the internal consistency of the given data sets are based on common patterns exhibited by tropospheric NMHCs. All tests show that samples from the first flights do not comply with the expected pattern. Additionally, detected alkene artefacts suggest potential problems with storing or contamination within all measurement flights. Two last flights # 130-133 and # 166-169 comply with the tests therefore their detailed analysis is made. Samples were analyzed in terms of their origin (troposphere vs. stratosphere, backward trajectories), their aging (NMHCs ratios) and detected plumes were compared to chemical signatures of Asian outflows. In the last chapter a future development of the presented system with focus on separation is drawn. An extensive appendix documents all important aspects of the dissertation from theoretical introduction through illustration of sample treatment to overview diagrams for the measured flights.

Automatic induction of lexical features

This thesis concerns artificially intelligent natural language processing systems that are capable of learning the properties of lexical items (properties like verbal valency or inflectional class membership) autonomously while they are fulfilling their tasks for which they have been deployed in the first place. Many of these tasks require a deep analysis of language input, which can be characterized as a mapping of utterances in a given input C to a set S of linguistically motivated structures with the help of linguistic information encoded in a grammar G and a lexicon L: G + L + C → S (1) The idea that underlies intelligent lexical acquisition systems is to modify this schematic formula in such a way that the system is able to exploit the information encoded in S to create a new, improved version of the lexicon: G + L + S → L' (2) Moreover, the thesis claims that a system can only be considered intelligent if it does not just make maximum usage of the learning opportunities in C, but if it is also able to revise falsely acquired lexical knowledge. So, one of the central elements in this work is the formulation of a couple of criteria for intelligent lexical acquisition systems subsumed under one paradigm: the Learn-Alpha design rule. The thesis describes the design and quality of a prototype for such a system, whose acquisition components have been developed from scratch and built on top of one of the state-of-the-art Head-driven Phrase Structure Grammar (HPSG) processing systems. The quality of this prototype is investigated in a series of experiments, in which the system is fed with extracts of a large English corpus. While the idea of using machine-readable language input to automatically acquire lexical knowledge is not new, we are not aware of a system that fulfills Learn-Alpha and is able to deal with large corpora. To instance four major challenges of constructing such a system, it should be mentioned that a) the high number of possible structural descriptions caused by highly underspeci ed lexical entries demands for a parser with a very effective ambiguity management system, b) the automatic construction of concise lexical entries out of a bulk of observed lexical facts requires a special technique of data alignment, c) the reliability of these entries depends on the system's decision on whether it has seen 'enough' input and d) general properties of language might render some lexical features indeterminable if the system tries to acquire them with a too high precision. The cornerstone of this dissertation is the motivation and development of a general theory of automatic lexical acquisition that is applicable to every language and independent of any particular theory of grammar or lexicon. This work is divided into five chapters. The introductory chapter first contrasts three different and mutually incompatible approaches to (artificial) lexical acquisition: cue-based queries, head-lexicalized probabilistic context free grammars and learning by unification. Then the postulation of the Learn-Alpha design rule is presented. The second chapter outlines the theory that underlies Learn-Alpha and exposes all the related notions and concepts required for a proper understanding of artificial lexical acquisition. Chapter 3 develops the prototyped acquisition method, called ANALYZE-LEARN-REDUCE, a framework which implements Learn-Alpha. The fourth chapter presents the design and results of a bootstrapping experiment conducted on this prototype: lexeme detection, learning of verbal valency, categorization into nominal count/mass classes, selection of prepositions and sentential complements, among others. The thesis concludes with a review of the conclusions and motivation for further improvements as well as proposals for future research on the automatic induction of lexical features.