An investigation into the tropospheric chemistry of acidic aerosols and ammonia in the laboratory

This thesis describes a broad range of experiments based on an aerosol flow-tube system to probe the interactions between atmospherically relevant aerosols with trace gases. This apparatus was used to obtain simultaneous optical and size distribution measurements using FTIR and SMPS measurements respectively as a function of relative humidity and aerosol chemical composition. Heterogeneous reactions between various ratios of ammonia gas and acidic aerosols were studied in aerosol form as opposed to bulk solutions. The apparatus is unique, in that it employed two aerosol generation methods to follow the size evolution of the aerosol while allowing detailed spectroscopic investigation of its chemical content. A novel chemiluminescence apparatus was also used to measure [NH4+]. SO2.H2O is an important species as it represents the first intermediate in the overall atmospheric oxidation process of sulfur dioxide to sulfuric acid. This complex was produced within gaseous, aqueous and aerosol SO2 systems. The addition of ammonia, gave mainly hydrogen sulfite tautomers and disulfite ions. These species were prevalent at high humidities enhancing the aqueous nature of sulfur (IV) species. Their weak acidity is evident due to the low [NH4+] produced. An increasing recognition that dicarboxylic acids may contribute significantly to the total acid burden in polluted urban environments is evident in the literature. It was observed that speciation within the oxalic, malonic and succinic systems shifted towards the most ionised form as the relative humidity was increased due to complete protonisation. The addition of ammonia produced ammonium dicarboxylate ions. Less reaction for ammonia with the malonic and succinic species were observed in comparison to the oxalic acid system. This observation coincides with the decrease in acidity of these organic species. The interaction between dicarboxylic acids and ‘sulfurous’/sulfuric acid has not been previously investigated. Therefore the results presented here are original to the field of tropospheric chemistry. SHO3-; S2O52-; HSO4-; SO42- and H1,3,5C2,3,4O4-;C2,3,4O4 2- were the main components found in the complex inorganic-organic systems investigated here. The introduction of ammonia produced ammonium dicarboxylate as well as ammonium disulfite/sulfate ions and increasing the acid concentrations increased the total amount of [NH4+].

Neonatal EEG classification using atomic decomposition

The electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant. It provides an excellent assessment of at-risk newborns and formulates a prognosis for long-term neurologic outcome.The automated analysis of neonatal EEG data in the NICU can provide valuable information to the clinician facilitating medical intervention. The aim of this thesis is to develop a system for automatic classification of neonatal EEG which can be mainly divided into two parts: (1) classification of neonatal EEG seizure from nonseizure, and (2) classifying neonatal background EEG into several grades based on the severity of the injury using atomic decomposition. Atomic decomposition techniques use redundant time-frequency dictionaries for sparse signal representations or approximations. The first novel contribution of this thesis is the development of a novel time-frequency dictionary coherent with the neonatal EEG seizure states. This dictionary was able to track the time-varying nature of the EEG signal. It was shown that by using atomic decomposition and the proposed novel dictionary, the neonatal EEG transition from nonseizure to seizure states could be detected efficiently. The second novel contribution of this thesis is the development of a neonatal seizure detection algorithm using several time-frequency features from the proposed novel dictionary. It was shown that the time-frequency features obtained from the atoms in the novel dictionary improved the seizure detection accuracy when compared to that obtained from the raw EEG signal. With the assistance of a supervised multiclass SVM classifier and several timefrequency features, several methods to automatically grade EEG were explored. In summary, the novel techniques proposed in this thesis contribute to the application of advanced signal processing techniques for automatic assessment of neonatal EEG recordings.