6 resultados para cooking-generated aerosol

em Cochin University of Science


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School of Environmental Studies, Cochin University of Science and Technology

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Time and space resolved studies of emission from CN molecules have been carried out in the plasma produced from graphite target by 1.06 urn pulses from a Q-switched Nd:YAG laser. Depending on the laser pulse energy, time of observation and position of the sampled volume of the plasma, the features of the emission spectrum are found to change drastically. The vibrational temperature and population distribution in the different vibrational levels have been studied as functions of distance, time, laser energy and ambient gas pressure. Evidence for nonlinear effects of the plasma medium such as self focusing which exhibits threshold-like behaviour are also obtained. Temperature and electron density of the plasma have been evaluated using the relative line intensities of successive ionization stages of carbon atom. These electron density measurements are verified by using Stark broadening method.

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The present study brings out the influence of transport dynamics on the aerosol distribution over the Indian region at a few selected geographically distinct locations. Over the Bay of Bengal the dominant pathway of aerosol transport during the pre-monsoon period is through higher altitudes (~ 3 km); directed from the Indian main land. In contrast, the aerosol pathways over the Arabian Sea during the same period are quite complex. They are directed from geographically different environments around the ocean through different altitudes. However in general, the day-to-day variability of AOD at both these regions is significantly influenced by the features of atmospheric circulation especially, the wind convergence at higher altitudes (around 3 km). Over the Ganga Basin during the winter period, the wind convergence at lower altitudes (< I km) govems the shon term variations in AOD, while the mean AOD distribution at this location is mainly governed by the local anthropogenic sources.

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The objective of this study is to understand the reasons for the enhancement in aerosol optical depth (AOD) over the Arabian Sea observed during June, July and August. During these months, high values of AOD are found over the sea beyond 10◦ N and adjacent regions. The Arabian Sea is bounded by the lands of Asia and Africa on its three sides. So the region is influenced by transported aerosols from the surroundings as well as aerosols of local origin (marine aerosols). During the summer monsoon season in India, strong surface winds with velocities around 15 m s−1 are experienced over most parts of the Arabian Sea. These winds are capable of increasing sea spray activity, thereby enhancing the production of marine aerosols. The strong winds increase the contribution of marine aerosols over the region to about 60% of the total aerosol content. The main components of marine aerosols include sea salt and sulphate particles. The remaining part of the aerosol particles comes from the western and northern land masses around the sea, of which the main component is transported dust particles. This transport is observed at higher altitudes starting from 600 m. At low levels, the transport occurs mainly from the Indian Ocean and the Arabian Sea itself, indicating the predominance of marine aerosols at these levels. The major portion of the total aerosol loading was contributed by coarse-mode particles during the period of study. But in the winter season, the concentration of coarse-mode aerosols is found to be less. From the analysis, it is concluded that the increase in marine aerosols and dust particles transported from nearby deserts results in an increase in aerosol content over the Arabian Sea during June, July and August.

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The classical methods of analysing time series by Box-Jenkins approach assume that the observed series uctuates around changing levels with constant variance. That is, the time series is assumed to be of homoscedastic nature. However, the nancial time series exhibits the presence of heteroscedasticity in the sense that, it possesses non-constant conditional variance given the past observations. So, the analysis of nancial time series, requires the modelling of such variances, which may depend on some time dependent factors or its own past values. This lead to introduction of several classes of models to study the behaviour of nancial time series. See Taylor (1986), Tsay (2005), Rachev et al. (2007). The class of models, used to describe the evolution of conditional variances is referred to as stochastic volatility modelsThe stochastic models available to analyse the conditional variances, are based on either normal or log-normal distributions. One of the objectives of the present study is to explore the possibility of employing some non-Gaussian distributions to model the volatility sequences and then study the behaviour of the resulting return series. This lead us to work on the related problem of statistical inference, which is the main contribution of the thesis