Greenhouse gas fluxes and mitigation potential for managed lands in the Russian Federation

Mitigation and Adaptation Strategies for Global Change - This work aims to assess the dynamics of net greenhouse gas (GHG) emissions and removals, as well as analyse the mitigation potential for...     

Assessment of microbiological and parasitological quality of composted wastes: health implications and hygienic measures.

Feedstock and compost samples were collected from twenty composting plants and analysed from the microbiological point of view. Faecal indicator organisms were determined in order to evaluate the efficacy of processes for the removal of pathogenic micro-organisms with similar survival characteristics and to verify their suitability as appropriate markers of microbial quality of composted products. In addition to the classical bacterial indicators, selected organisms, such as Salmonella, Giardia, Cryptosporidium, Clostridium spores and helminth ova, were investigated. Statistically significant differences in the removal of the different micro-organisms were observed with regard to both the different composting plants (P < 0.05) and feedstock composition (P < 0.05). In fact, compost obtained by feedstock containing sewage sludge was shown to have a better hygienic quality in comparison with compost containing green discards and municipal solid waste as raw matter. Giardia cysts, Cryptosporidium oocysts and helminth ova were not effective indicators of hygienic quality of compost, whereas Clostridium perfringens spores, because of their high resistance to treatments, could be considered as an additional model for assessing the composting process, especially with regard to more resistant pathogen reduction.     

A geostatistical and probabilistic spectral image processing methodology for monitoring potential CO2 leakages on the surface

Remote sensing has demonstrated success in various environmental applications over the past three decades. This is largely attributed to its ability for good areal coverage and continued development in sensor technologies. Carbon dioxide Capture and Storage (CCS) is an emerging climate change mitigation technology where monitoring is vital for its sustainability. This research investigates the use of spectral remote sensing imagery in detecting potential CO₂ occurrences at the surface, should a leakage occur from subsurface reservoirs where CO₂ is stored. Currently, there are no known leakages of CO₂ at industrial storage sites, therefore, this research was carried out at the Latera natural analogue site in Italy, in order to develop the methodology described. This paper describes the use of a popular probabilistic information fusion theory, referred to as the Dempster–Shafer theory of evidence, to analyse outlier pixels (anomalies). Outlier pixels are first determined using a new geostatistical image filtering methodology based on Intrinsic Random Function (IRF), Independent Component Analysis (ICA), and the industry standard parametric Reed–Xiaoli (RX) anomaly detection. Information fusion of detected outlier pixels and indirect surface effects of CO₂ leakage over time, such as stressed vegetation or mineral alterations, assigns a confidence measure per outlier pixel in order to identify potential leakage points. After visual validation using direct field measurements, it was demonstrated that the proposed methodology is able to detect majority of the seepage points at Latera, and holds promise as a new unsupervised CO₂ monitoring methodology.     

Individual-based modelling of carbon and nitrogen dynamics in soils: Parameterization and sensitivity analysis of microbial components

The fate of soil carbon and nitrogen compounds in soils in response to climate change is currently the object of significant research. In particular, there is much interest in the development of a new generation of micro-scale models of soil ecosystems processes. Crucial to the elaboration of such models is the ability to describe the growth and metabolism of small numbers of individual microorganisms, distributed in a highly heterogeneous environment. In this context, the key objective of the research described in this article was to further develop an individual-based soil organic matter model, INDISIM-SOM, first proposed a few years ago, and to assess its performance with a broader experimental data set than previously considered. INDISIM-SOM models the dynamics and evolution of carbon and nitrogen associated with organic matter in soils. The model involves a number of state variables and parameters related to soil organic matter and microbial activity, including growth and decay of microbial biomass, temporal evolutions of easily hydrolysable N, mineral N in ammonium and nitrate, CO₂ and O₂. The present article concentrates on the biotic components of the model. Simulation results demonstrate that the model can be calibrated to provide good fit to experimental data from laboratory incubation experiments performed on three different types of Mediterranean soils. In addition, analysis of the sensitivity toward its biotic parameters shows that the model is far more sensitive to some parameters, i.e., the microbial maintenance energy and the probability of random microbial death, than to others. These results suggest that, in the future, research should focus on securing better measurements of these parameters, on environmental determinants of the switch from active to dormant states, and on the causes of random cell death in soil ecosystems.