Effect of forest-based biofuels production on carbon footprint, Case: Integrated LWC paper mill

International energy and climate strategies also set Finland’s commitments to increasing the use of renewable energy sources and reducing greenhouse gas emissions. The target can be achieved by, for example, increasing the use of energy wood. Finland’s forest biomass potential is significant compared with current use. Increased use will change forest management and wood harvesting methods however. The thesis examined the potential for integrated pulp and paper mills to increase bioenergy production. The effects of two bioenergy production technologies on the carbon footprint of an integrated LWC mill were studied at mill level and from the cradle-to-customer approach. The LignoBoost process and FT diesel production were chosen as bioenergy cases. The data for the LignoBoost process were obtained from Metso and for the FT diesel process from Neste Oil. The rest of the information is based on the literature and databases of the KCL-ECO life-cycle computer program and Ecoinvent. In both case studies, the carbon footprint was reduced. From the results, it can be concluded that it is possible to achieve a fossil-fuel-free pulp mill with the LignoBoost process. By using steam from the FT diesel process, the amount of auxiliary fuel can be reduced considerably and the bark boiler can be replaced. With a choice of auxiliary fuels for use in heat production in the paper mill and the production methods for purchased electricity, it is possible to affect the carbon footprints even more in both cases.

Early Detection of Alzheimer's Disease Beta-amyloid Pathology -Applicability of Positron Emission Tomography with the Amyloid Radioligand 11C-PIB

Early Detection of Alzheimer's Disease Beta-amyloid Pathology -Applicability of Positron Emission Tomography with the Amyloid Radioligand ¹¹C-PIB Accumulation of beta amyloid (Abeta) in the brain is characteristic for Alzheimer’s disease (AD). Carbon-11 labeled 2-(4’-methylaminophenyl)-6-hydroxybenzothiazole (¹¹C-PIB) is a novel positron emission tomography (PET) amyloid imaging agent that appears to be applicable for in vivo Abeta plaque detection and quantitation. The biodistribution and radiation dosimetry of ¹¹C-PIB were investigated in 16 healthy subjects. The reproducibility of a simplified ¹¹C-PIB quantitation method was evaluated with a test-retest study on 6 AD patients and 4 healthy control subjects. Brain ¹¹C-PIB uptake and its possible association with brain atrophy rates were studied over a two-year follow-up in 14 AD patients and 13 healthy controls. Nine monozygotic and 8 dizygotic twin pairs discordant for cognitive impairment and 9 unrelated controls were examined to determine whether brain Abeta accumulation could be detected with ¹¹C-PIB PET in cognitively intact persons who are at increased genetic risk for AD. The highest absorbed radiation dose was received by the gallbladder wall (41.5 mjuGy/MBq). About 20 % of the injected radioactivity was excreted into urine, and the effective whole-body radiation dose was 4.7 mjuSv/MBq. Such a dose allows repeated scans of individual subjects. The reproducibility of the simplified ¹¹C-PIB quantitation was good or excellent both at the regional level (VAR 0.9-5.5 %) and at the voxel level (VAR 4.2-6.4 %). ¹¹C-PIB uptake did not increase during 24 months’ follow-up of subjects with mild or moderate AD, even though brain atrophy and cognitive decline progressed. Baseline neocortical ¹¹C-PIB uptake predicted subsequent volumetric brain changes in healthy control subjects (r = 0.725, p = 0.005). Cognitively intact monozygotic co-twins – but not dizygotic co-twins – of memory-impaired subjects exhibited increased ¹¹C-PIB uptake (117-121 % of control mean) in their temporal and parietal cortices and the posterior cingulate (p<0.05), when compared with unrelated controls. This increased uptake may be representative of an early AD process, and genetic factors seem to play an important role in the development of AD-like Abeta plaque pathology. ¹¹C-PIB PET may be a useful method for patient selection and follow-up for early-phase intervention trials of novel therapeutic agents. AD might be detectable in high-risk individuals in its presymptomatic stage with ¹¹C-PIB PET, which would have important consequences both for future diagnostics and for research on disease-modifying treatments.

Automated, adaptive methods for forest inventory

Forest inventories are used to estimate forest characteristics and the condition of forest for many different applications: operational tree logging for forest industry, forest health state estimation, carbon balance estimation, land-cover and land use analysis in order to avoid forest degradation etc. Recent inventory methods are strongly based on remote sensing data combined with field sample measurements, which are used to define estimates covering the whole area of interest. Remote sensing data from satellites, aerial photographs or aerial laser scannings are used, depending on the scale of inventory. To be applicable in operational use, forest inventory methods need to be easily adjusted to local conditions of the study area at hand. All the data handling and parameter tuning should be objective and automated as much as possible. The methods also need to be robust when applied to different forest types. Since there generally are no extensive direct physical models connecting the remote sensing data from different sources to the forest parameters that are estimated, mathematical estimation models are of "black-box" type, connecting the independent auxiliary data to dependent response data with linear or nonlinear arbitrary models. To avoid redundant complexity and over-fitting of the model, which is based on up to hundreds of possibly collinear variables extracted from the auxiliary data, variable selection is needed. To connect the auxiliary data to the inventory parameters that are estimated, field work must be performed. In larger study areas with dense forests, field work is expensive, and should therefore be minimized. To get cost-efficient inventories, field work could partly be replaced with information from formerly measured sites, databases. The work in this thesis is devoted to the development of automated, adaptive computation methods for aerial forest inventory. The mathematical model parameter definition steps are automated, and the cost-efficiency is improved by setting up a procedure that utilizes databases in the estimation of new area characteristics.