Markov random fields and spatial smoothing in improving of forest inventory estimates

Markov random fields (MRF) are popular in image processing applications to describe spatial dependencies between image units. Here, we take a look at the theory and the models of MRFs with an application to improve forest inventory estimates. Typically, autocorrelation between study units is a nuisance in statistical inference, but we take an advantage of the dependencies to smooth noisy measurements by borrowing information from the neighbouring units. We build a stochastic spatial model, which we estimate with a Markov chain Monte Carlo simulation method. The smooth values are validated against another data set increasing our confidence that the estimates are more accurate than the originals.

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.