The uncertainty of the climate and health effects of aerosols is still large, one important reason being lack of knowledge of the primary emissions. This thesis combines laboratory and field experiments, and process modelling in an effort to better quantify the submicrometre aerosol emissions and to understand some of the processes in the atmosphere. A parameterisation was derived for the source flux of sea salt particles (particles m-2 s-1) in the size range 0.02-2.8 µm dry diameter (Dp), it is the first parameterisation to almost cover two full decades of the submicrometre sea salt aerosol production, and to include the effect of water temperature. This sea salt parameterisation was validated for temperate water in the 0.1-1.1 μm Dp range using in situ size resolved emissions of marine aerosol particles, which were measured with the eddy covariance (EC) method. For periods sampled air was heated to 300ºC in order to evaporate semi-volatile organics and isolate the sea salt fraction. Comparisons with the total aerosol emissions suggest that in these emissions organic carbon and sea salt are internally mixed in the same particles. Finally an aerosol dynamics model was modified for marine conditions. In the model parameterised emissions of sea salt was included together with aerosol dynamics, chemistry and clouds representative for the marine boundary layer. The sea salt emissions are together with secondary sulphate, and cloud processing able to reproduce a typical marine aerosol size distribution and cloud condensation nuclei population. Measurements with the EC method of the road traffic related aerosol source number flux for Dp>0.011 µm show that the major part of the aerosol fluxes is due to road traffic emissions. Both an emission factor for the whole fleet mix in Stockholm (1.4x1014 vehicle-1 km-1) and separate emission factors for light- and heavy-duty vehicles (HDV) were deduced. The result shows that during weekdays 70-80% of the emissions come from HDV.

Carbon dioxide (CO2) is an important greenhouse gas, and the atmospheric concentration of CO2 has increased by more than 100 ppm since prior to the industrial revolution.  The global oceans are considered an important sink of atmospheric CO2, since approximately one third of the anthropogenic emissions are absorbed by the oceans. To be able to model the global carbon cycle and the future climate, it is important to have knowledge of the processes controlling the air-sea exchange of CO2. In this thesis, measurements as well as a model is used in order to increase the knowledge of the exchange processes. The air-sea flux of CO2 is estimated from high frequency measurements using three methods; one empirical method, and two methods with a solid theoretical foundation. The methods are modified to be applicable for various atmospheric stratifications, and the agreement between methods is good in average. A new parameterization of the transfer velocity (the rate of transfer across the air-sea interface), is implemented in a Baltic Sea model. The new parameterization includes also the mechanism of water-side convection. The impact of including the new parameterization is relatively small due to feedback processes in the model. The new parameterization is however more representative for flux calculations using in-situ measurement or remote sensing products. When removing the feedback to the model, the monthly average flux increases by up to 20% in some months, compared to when water-side convection is not included. The Baltic Sea carbon budget was estimated using the Baltic Sea model, and the Baltic Sea was found to be a net sink of CO2. This is consistent with some previous studies, while contradictory to others. The dissimilarity between studies indicates the difficulty in estimating the carbon budget mainly due to variations of the CO2 uptake/release in time and space. Local variations not captured by the model, such as coastal upwelling, give uncertainties to the model. Coastal upwelling can alter the uptake/release of CO2 in a region by up to 250%. If upwelling would be included in the model, the Baltic Sea might be considered a smaller sink of CO2.

Diploma thesis Business risks in insurance and their quantification describes the business risks to which insurance companies are exposed in their activities. Thesis is focused on market risk and quantification of market risk in insurance companies. It includes determination of the specifications for the activities of insurance companies, regulation and characteric of business risks in insurance. Large part of the thesis deals with the method of Value at Risk as a tool to measure market risk as well as individual methods to calculate it. In the conclusion, thesis describes the processes of quantification of market risk in Generali PPF Holding and in Česká poisťovňa, which gives a practical insight into the issues of market risk in insurance companies.

VINTERA, J. Spectral anlysis with superesolution. Brno: University of Technology, The Faculty of Electrical Engineering and Communication, 2008. 85 p. Master’s thesis. This thesis deals with the topic of super-resolution spectral analysis in the Signal Processing Toolset. The Signal Processing Toolset is a software component of the LabVIEW 8.1. program equipment. The thesis consists of three main parts. In the first part the basic theoretic concepts of the Model-Based Frequency Analysis are described. The second part serves as a user manual for the super-resolution spectral analysis in the Signal Processing Toolset. The last part describes the application of the theory introduced in the first part, by means of testing the properties of the methods used by the Toolset.

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風險值大多是在假設資產報酬為常態分配下計算而得的，但是這個假設與實際的資產報酬分配不一致，因為很多研究者都發現實際的資產報酬分配都有厚尾的現象，也就是極端事件的發生機率遠比常態假設要來的高，因此利用常態假設來計算風險值對於真實損失的衡量不是很恰當。 針對這個問題，本論文以歷史模擬法、變異數-共變異數法、混合常態模型來模擬報酬率的分配，並依給定的信賴水準估算出風險值，其中混合常態模型的參數是利用準貝式最大概似估計法及EM演算法來估計；然後利用三種風險值的評量方法：回溯測試、前向測試與二項檢定，來評判三種估算風險值方法的優劣。 經由實證結果發現： 1.報酬率分配在左尾臨界機率1％有較明顯厚尾的現象。 2.利用混合常態分配來模擬報酬率分配會比另外兩種方法更能準確的捕捉到左尾臨界機率1％的厚尾。 3.混合常態模型的峰態係數值接近於真實報酬率分配的峰態係數值，因此我們可以確認混合常態模型可以捕捉高峰的現象。 關鍵字：風險值、厚尾、歷史模擬法、變異數-共變異教法、混合常態模型、準貝式最大概似估計法、EM演算法、回溯測試、前向測試、高峰 / Initially, Value at Risk (VaR) is calculated by assuming that the underline asset return is normal distribution, but this assumption sometimes does not consist with the actual distribution of asset return. Many researchers have found that the actual distribution of the underline asset return have Fat-Tail, extreme value events, character. So under normal distribution assumption, the VaR value is improper compared with the actual losses. The paper discuss three methods. Historical Simulated method - Variance-Covariance method and Mixture Normal .simulating those asset, return and VaR by given proper confidence level. About the Mixture Normal Distribution, we use both EM algorithm and Quasi-Bayesian MLE calculating its parameters. Finally, we use tree VaR testing methods, Back test、Forward tes and Binomial test -----comparing its VaR loss probability We find the following results: 1.Under 1% left-tail critical probability, asset return distribution has significant Fat-tail character. 2.Using Mixture Normal distribution we can catch more Fat-tail character precisely than the other two methods. 3.The kurtosis of Mixture Normal is close to the actual kurtosis, this means that the Mixture Normal distribution can catch the Leptokurtosis phenomenon. Key words: Value at Risk、VaR、Fat tail、Historical simulation method、 Variance-Covariance method、Mixture Normal distribution、Quasi-Bayesian MLE、EM algorithm、Back test、 Forward test、 Leptokurtosis