Microbial activities in boreal soils: Biodegradation of organic contaminants at low temperature and ammonia oxidation

This thesis deals with the response of biodegradation of selected anthropogenic organic contaminants and natural autochthonous organic matter to low temperature in boreal surface soils. Furthermore, the thesis describes activity, diversity and population size of autotrophic ammonia-oxidizing bacteria (AOB) in a boreal soil used for landfarming of oil-refinery wastes, and presents a new approach, in which the particular AOB were enriched and cultivated in situ from the landfarming soil onto cation exchange membranes. This thesis demonstrates that rhizosphere fraction of natural forest humus soil and agricultural clay loam soil from Helsinki Metropolitan area were capable of degrading of low to moderate concentrations (0.2 50 µg cm-3) of PCP, phenanthrene and 2,4,5-TCP at temperatures realistic to boreal climate (-2.5 to +15 °C). At the low temperatures, the biodegradation of PCP, phenanthrene and 2,4,5-TCP was more effective (Q10-values from 1.6 to 7.6) in the rhizosphere fraction of the forest soil than in the agricultural soil. Q10-values of endogenous soil respiration (carbon dioxide evolution) and selected hydrolytic enzyme activities (acetate-esterase, butyrate-esterase and β-glucosidase) in acid coniferous forest soil were 1.6 to 2.8 at temperatures from -3 to +30 °C. The results indicated that the temperature dependence of decomposition of natural autochthonous soil organic matter in the studied coniferous forest was only moderate. The numbers of AOB in the landfarming (sandy clay loam) soil were determined with quantitative polymerase chain reaction (real-time PCR) and with Most Probable Number (MPN) methods, and potential ammonium oxidation activity was measured with the chlorate inhibition technique. The results indicated presence of large and active AOB populations in the heavily oil-contaminated and urea-fertilised landfarming soil. Assessment of the populations of AOB with denaturing gradient gel electrophoresis (DGGE) profiling and sequence analysis of PCR-amplified 16S rRNA genes showed that Nitrosospira-like AOB in clusters 2 and 3 were predominant in the oily landfarming soil. This observation was supported by fluorescence in situ hybridization (FISH) analysis of the AOB grown on the soil-incubated cation-exchange membranes. The results of this thesis expand the suggested importance of Nitrosospira-like AOB in terrestrial environments to include chronically oil-contaminated soils.

Country-scale carbon accounting of the vegetation and mineral soils of Finland

The increase in global temperature has been attributed to increased atmospheric concentrations of greenhouse gases (GHG), mainly that of CO2. The threat of severe and complex socio-economic and ecological implications of climate change have initiated an international process that aims to reduce emissions, to increase C sinks, and to protect existing C reservoirs. The famous Kyoto protocol is an offspring of this process. The Kyoto protocol and its accords state that signatory countries need to monitor their forest C pools, and to follow the guidelines set by the IPCC in the preparation, reporting and quality assessment of the C pool change estimates. The aims of this thesis were i) to estimate the changes in carbon stocks vegetation and soil in the forests in Finnish forests from 1922 to 2004, ii) to evaluate the applied methodology by using empirical data, iii) to assess the reliability of the estimates by means of uncertainty analysis, iv) to assess the effect of forest C sinks on the reliability of the entire national GHG inventory, and finally, v) to present an application of model-based stratification to a large-scale sampling design of soil C stock changes. The applied methodology builds on the forest inventory measured data (or modelled stand data), and uses statistical modelling to predict biomasses and litter productions, as well as a dynamic soil C model to predict the decomposition of litter. The mean vegetation C sink of Finnish forests from 1922 to 2004 was 3.3 Tg C a-1, and in soil was 0.7 Tg C a-1. Soil is slowly accumulating C as a consequence of increased growing stock and unsaturated soil C stocks in relation to current detritus input to soil that is higher than in the beginning of the period. Annual estimates of vegetation and soil C stock changes fluctuated considerably during the period, were frequently opposite (e.g. vegetation was a sink but soil was a source). The inclusion of vegetation sinks into the national GHG inventory of 2003 increased its uncertainty from between -4% and 9% to ± 19% (95% CI), and further inclusion of upland mineral soils increased it to ± 24%. The uncertainties of annual sinks can be reduced most efficiently by concentrating on the quality of the model input data. Despite the decreased precision of the national GHG inventory, the inclusion of uncertain sinks improves its accuracy due to the larger sectoral coverage of the inventory. If the national soil sink estimates were prepared by repeated soil sampling of model-stratified sample plots, the uncertainties would be accounted for in the stratum formation and sample allocation. Otherwise, the increases of sampling efficiency by stratification remain smaller. The highly variable and frequently opposite annual changes in ecosystem C pools imply the importance of full ecosystem C accounting. If forest C sink estimates will be used in practice average sink estimates seem a more reasonable basis than the annual estimates. This is due to the fact that annual forest sinks vary considerably and annual estimates are uncertain, and they have severe consequences for the reliability of the total national GHG balance. The estimation of average sinks should still be based on annual or even more frequent data due to the non-linear decomposition process that is influenced by the annual climate. The methodology used in this study to predict forest C sinks can be transferred to other countries with some modifications. The ultimate verification of sink estimates should be based on comparison to empirical data, in which case the model-based stratification presented in this study can serve to improve the efficiency of the sampling design.