Molecular composition of biogenic secondary organic aerosols using ultrahigh resolution mass spectrometry: comparing laboratory and field studies

Numerous laboratory experiments have been performed in an attempt to mimic atmospheric secondary organic aerosol (SOA) formation. However, it is still unclear how close the aerosol particles generated in laboratory experiments resemble atmospheric SOA with respect to their detailed chemical composition. In this study, we generated SOA in a simulation chamber from the ozonolysis of α-pinene and a biogenic volatile organic compound (BVOC) mixture containing α- and β-pinene, Δ3-carene, and isoprene. The detailed molecular composition of laboratory-generated SOA was compared with that of background ambient aerosol collected at a boreal forest site (Hyytiälä, Finland) and an urban location (Cork, Ireland) using direct infusion nanoelectrospray ultrahigh resolution mass spectrometry. Kendrick Mass Defect and Van Krevelen approaches were used to identify and compare compound classes and distributions of the detected species. The laboratory-generated SOA contained a distinguishable group of dimers that was not observed in the ambient samples. The presence of dimers was found to be less pronounced in the SOA from the VOC mixtures when compared to the one component precursor system. The elemental composition of the compounds identified in the monomeric region from the ozonolysis of both α-pinene and VOC mixtures represented the ambient organic composition of particles collected at the boreal forest site reasonably well, with about 70% of common molecular formulae. In contrast, large differences were found between the laboratory-generated BVOC samples and the ambient urban sample. To our knowledge this is the first direct comparison of molecular composition of laboratory-generated SOA from BVOC mixtures and ambient samples.

An investigation on the use of SNR distributions for the optimisation of coarse-fine spectrum sensing for cognitive radio

This thesis investigates the optimisation of Coarse-Fine (CF) spectrum sensing architectures under a distribution of SNRs for Dynamic Spectrum Access (DSA). Three different detector architectures are investigated: the Coarse-Sorting Fine Detector (CSFD), the Coarse-Deciding Fine Detector (CDFD) and the Hybrid Coarse-Fine Detector (HCFD). To date, the majority of the work on coarse-fine spectrum sensing for cognitive radio has focused on a single value for the SNR. This approach overlooks the key advantage that CF sensing has to offer, namely that high powered signals can be easily detected without extra signal processing. By considering a range of SNR values, the detector can be optimised more effectively and greater performance gains realised. This work considers the optimisation of CF spectrum sensing schemes where the security and performance are treated separately. Instead of optimising system performance at a single, constant, low SNR value, the system instead is optimised for the average operating conditions. The security is still provided such that at the low SNR values the safety specifications are met. By decoupling the security and performance, the system’s average performance increases whilst maintaining the protection of licensed users from harmful interference. The different architectures considered in this thesis are investigated in theory, simulation and physical implementation to provide a complete overview of the performance of each system. This thesis provides a method for estimating SNR distributions which is quick, accurate and relatively low cost. The CSFD is modelled and the characteristic equations are found for the CDFD scheme. The HCFD is introduced and optimisation schemes for all three architectures are proposed. Finally, using the Implementing Radio In Software (IRIS) test-bed to confirm simulation results, CF spectrum sensing is shown to be significantly quicker than naive methods, whilst still meeting the required interference probability rates and not requiring substantial receiver complexity increases.