Bioremediation of diesel oil contaminated soil and water

Diesel spills contaminate aquatic and terrestrial environments. To prevent the environmental and health risks, the remediation needs to be advanced. Bioremediation, i.e., degradation by microbes, is one of the suitable methods for cleaning diesel contamination. In monitored natural attenuation technique are natural processes in situ combined, including bioremediation, volatilization, sorption, dilution and dispersion. Soil bacteria are capable of adapting to degrade environmental pollutants, but in addition, some soil types may have indigenous bacteria that are naturally suitable for degradation. The objectives for this work were (1) to find a feasible and economical technique to remediate oil spilled into Baltic Sea water and (2) to bioremediate soil contaminated by diesel oil. Moreover, the aim was (3) to study the potential for natural attenuation and the indigenous bacteria in soil, and possible adaptation to degrade diesel hydrocarbons. In the aquatic environment, the study concentrated on diesel oil sorption to cotton grass fiber, a natural by-product of peat harvesting. The impact of diesel pollution was followed in bacteria, phytoplankton and mussels. In a terrestrial environment, the focus was to compare the methods of enhanced biodegradation (biostimulation and bioaugmentation), and to study natural attenuation of oil hydrocarbons in different soil types and the effect that a history of previous contamination may have on the bioremediation potential. (1) In the aquatic environment, rapid removal of diesel oil was significant for survival of tested species and thereby diversity maintained. Cotton grass not only absorbed the diesel but also benefited the bacterial growth by providing a large colonizable surface area and hence oil-microbe contact area. Therefore use of this method would enhance bioremediation of diesel spills. (2) Biostimulation enhances bioremediation, and (3) indigenous diesel-degrading bacteria are present in boreal environments, so microbial inocula are not always needed. In the terrestrial environment experiments, the combination of aeration and addition of slowly released nitrogen advanced the oil hydrocarbon degradation. Previous contamination of soil gives the bacterial community the potential for rapid adaptation and efficient degradation of the same type of contaminant. When the freshly contaminated site needs addition of diesel degraders, previously contaminated and remediated soil could be used as a bacterial inoculum. Another choice of inoculum could be conifer forest soil, which provides a plentiful population of degraders, and based on the present results, could be considered as a safe non-polluted inoculum. According to the findings in this thesis, bioremediation (microbial degradation) and monitored natural attenuation (microbial, physical and chemical degradation) are both suitable techniques for remediation of diesel-contaminated sites in Finland.

Methods and applications for improving parameter prediction models for stand structures in Finland

This thesis report attempts to improve the models for predicting forest stand structure for practical use, e.g. forest management planning (FMP) purposes in Finland. Comparisons were made between Weibull and Johnson s SB distribution and alternative regression estimation methods. Data used for preliminary studies was local but the final models were based on representative data. Models were validated mainly in terms of bias and RMSE in the main stand characteristics (e.g. volume) using independent data. The bivariate SBB distribution model was used to mimic realistic variations in tree dimensions by including within-diameter-class height variation. Using the traditional method, diameter distribution with the expected height resulted in reduced height variation, whereas the alternative bivariate method utilized the error-term of the height model. The lack of models for FMP was covered to some extent by the models for peatland and juvenile stands. The validation of these models showed that the more sophisticated regression estimation methods provided slightly improved accuracy. A flexible prediction and application for stand structure consisted of seemingly unrelated regression models for eight stand characteristics, the parameters of three optional distributions and Näslund s height curve. The cross-model covariance structure was used for linear prediction application, in which the expected values of the models were calibrated with the known stand characteristics. This provided a framework to validate the optional distributions and the optional set of stand characteristics. Height distribution is recommended for the earliest state of stands because of its continuous feature. From the mean height of about 4 m, Weibull dbh-frequency distribution is recommended in young stands if the input variables consist of arithmetic stand characteristics. In advanced stands, basal area-dbh distribution models are recommended. Näslund s height curve proved useful. Some efficient transformations of stand characteristics are introduced, e.g. the shape index, which combined the basal area, the stem number and the median diameter. Shape index enabled SB model for peatland stands to detect large variation in stand densities. This model also demonstrated reasonable behaviour for stands in mineral soils.

Light scattering in random media with wavelength-scale structures : astronomical and industrial applications

Light scattering, or scattering and absorption of electromagnetic waves, is an important tool in all remote-sensing observations. In astronomy, the light scattered or absorbed by a distant object can be the only source of information. In Solar-system studies, the light-scattering methods are employed when interpreting observations of atmosphereless bodies such as asteroids, atmospheres of planets, and cometary or interplanetary dust. Our Earth is constantly monitored from artificial satellites at different wavelengths. With remote sensing of Earth the light-scattering methods are not the only source of information: there is always the possibility to make in situ measurements. The satellite-based remote sensing is, however, superior in the sense of speed and coverage if only the scattered signal can be reliably interpreted. The optical properties of many industrial products play a key role in their quality. Especially for products such as paint and paper, the ability to obscure the background and to reflect light is of utmost importance. High-grade papers are evaluated based on their brightness, opacity, color, and gloss. In product development, there is a need for computer-based simulation methods that could predict the optical properties and, therefore, could be used in optimizing the quality while reducing the material costs. With paper, for instance, pilot experiments with an actual paper machine can be very time- and resource-consuming. The light-scattering methods presented in this thesis solve rigorously the interaction of light and material with wavelength-scale structures. These methods are computationally demanding, thus the speed and accuracy of the methods play a key role. Different implementations of the discrete-dipole approximation are compared in the thesis and the results provide practical guidelines in choosing a suitable code. In addition, a novel method is presented for the numerical computations of orientation-averaged light-scattering properties of a particle, and the method is compared against existing techniques. Simulation of light scattering for various targets and the possible problems arising from the finite size of the model target are discussed in the thesis. Scattering by single particles and small clusters is considered, as well as scattering in particulate media, and scattering in continuous media with porosity or surface roughness. Various techniques for modeling the scattering media are presented and the results are applied to optimizing the structure of paper. However, the same methods can be applied in light-scattering studies of Solar-system regoliths or cometary dust, or in any remote-sensing problem involving light scattering in random media with wavelength-scale structures.