Environmental context In the coastal and ocean environment, oil spills and ship movement can produce hazardous, organic aerosols. In this study, the role of sea salt in the formation processes of crude-oil-derived organic aerosols derived was explored, and it was found that sea salt can greatly increase the formation and growth of these toxic aerosols. Understanding of this process is crucial for evaluating the effect of oil spills and ship movements on air quality and human health. Abstract Dual, large (52m3), outdoor chambers were used to investigate the effect of aerosol aqueous phase chemistry on the secondary organic aerosol (SOA) yields of the photooxidation products of aromatic hydrocarbons in the coastal environment. Toluene and 1,3,5-trimethylbenzene were photochemically oxidised in the presence and absence of inorganic seeds (sea salt aerosol (SSA) or NaCl) at low NOx conditions. Overall, the presence of SSA, which was shown to contain water even at low relative humidities (RHs), led to higher SOA yields than the presence of NaCl seeds and the seedless condition. The results suggest that SOA yields in the coastal environment will be higher than those produced in terrestrial environment. To study the effect of SOA formation on the chemical composition of SSA, inorganic species were measured using a particle-into-liquid-sampler coupled to an ion chromatograph. The hygroscopic properties of the SSA internally mixed with SOA were analysed using a Fourier-transform infrared spectrometer. The fresh SSA shows a weak phase transition whereas no clear phase transition appeared in the aged SSA. The depletion of Cl– due to the accommodation of nitric acid and carboxylic acids on the surface of SSA coincides with changes in aerosol hygroscopic properties.

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Abstract-This paper introduces a new system for real-time detection and classification of arbitrarily scattered surface-laid mines from multispectral imagery data of a minefield. The system consists of six channels which use various neural-network structures for feature extraction, detection, and classification of targets in six different optical bands ranging from near UV to near IR. A single-layer autoassociative network trained using the recursive least square (RLS) learning rule was employed in each channel to perform feature extraction. Based upon the extracted features, two different neural-network architectures were used and their performance was compared against the standard maximum likelihood (ML) classification scheme. The outputs of the detector/classifier network in all the channels were fused together in a final decision-making system. Two different final decision making schemes using the majority voting and weighted combination based on consensual theory were considered. Simulations were performed on real data for six bands and on several images in order to account for the variations in size, shape, and contrast of the targets and also the signalto-clutter ratio. The overall results showed the promise of the proposed system for detection and classification of mines and minelike tagets.

The development of a simple model of the seawater inherent optical properties (IOPs) associated with bubbles and sediments would represent a great advance in surf zone optics. We present one solution for this problem using a combination of geometrical optics and Fraunhofer diffraction. An analytic model of the IOPs of bubbles and sediments (the extinction and absorption coefficients, and phase function) is developed in terms of the moments of the particle size distribution and the complex refractive index of particles.

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