Silvena Boteva

Community level physiological profiles of bacterial communities inhabiting uranium mining impacted sites.

Bacterial activity and physiological diversity were characterized in mining and milling impacted soils collected from three abandoned uranium mine sites, Senokos, Buhovo and Sliven, using bacterial dehydrogenase activity and Biolog (EcoPlate) tests. The elemental composition of soils revealed high levels of uranium and heavy metals (sum of technogenic coefficients of contamination; TCC(sum) pollution as follows: Sliven (uranium - 374 mg/kg; TCC(sum) - 23.40) >Buhovo (uranium - 139.20mg/kg; TCC(sum) - 3.93) >Senokos (uranium - 23.01 mg/kg; TCC(sum) - 0.86). The physiological profiles of the bacterial community level were site specific, and indicated intensive utilization of polyols, carbohydrates and carboxylic acids in low and medium polluted environments, and i-erithrytol and 2-hydroxy-benzoic acid in the highly polluted environment of Sliven waste pile. Enzymes which take part in the biodegradation of recalcitrant substances were more resistant to pollution than these from the pathways of the easily degradable carbon sources. The Shannon index indicated that the physiological diversity of bacteria was site specific but not in line with the levels of pollution. A general tendency of increasing the importance of the number of utilizable substrates to bacterial physiological diversity was observed at less polluted sites, whereas in highly polluted sites the evenness of substrate utilization rate was more significant. Dehydrogenase activity was highest in Senokos upper soil layer and positively correlated (p<0.01) with the soil organic matter content. The bacterial activity (EcoPlate) and physiological diversity (Shannon index) correlated significantly and negatively with As, Cu, Zn, Pb and U, and Co, Cr, Ni and Mn, respectively. We concluded that the observed site specific shifts in bacterial communities were complex due to both the environmental peculiarities and the bacterial tolerance to the relevant level of pollution, rather than a strong indication of uranium and heavy metals toxicity.

Effects of long-term radionuclide and heavy metal contamination on the activity of microbial communities, inhabiting uranium mining impacted soils

Ore mining and processing have greatly altered ecosystems, often limiting their capacity to provide ecosystem services critical to our survival. The soil environments of two abandoned uranium mines were chosen to analyze the effects of long-term uranium and heavy metal contamination on soil microbial communities using dehydrogenase and phosphatase activities as indicators of metal stress. The levels of soil contamination were low, ranging from ‘precaution’ to ‘moderate’, calculated as Nemerow index. Multivariate analyses of enzyme activities revealed the following: (i) spatial pattern of microbial endpoints where the more contaminated soils had higher dehydrogenase and phosphatase activities, (ii) biological grouping of soils depended on both the level of soil contamination and management practice, (iii) significant correlations between both dehydrogenase and alkaline phosphatase activities and soil organic matter and metals (Cd, Co, Cr, and Zn, but not U), and (iv) multiple relationships between the alkaline than the acid phosphatase and the environmental factors. The results showed an evidence of microbial tolerance and adaptation to the soil contamination established during the long-term metal exposure and the key role of soil organic matter in maintaining high microbial enzyme activities and mitigating the metal toxicity. Additionally, the results suggested that the soil microbial communities are able to reduce the metal stress by intensive phosphatase synthesis, benefiting a passive environmental remediation and provision of vital ecosystem services.

Natural Communities of Uranium Mining Impacted Area in the Vicinity of the Senokos Village

ABSTRACT Uranium containing wastes accumulated during mine activities have resulted in a multitude of contaminated sites in Bulgaria. The lack of biomonitoring programs limits the environmental impact assessment of uranium impacted areas. The aim of the study was to investigate the health of natural terrestrial (vegetation, soil bacteria and millipedes) and freshwater (macrozoobenthos) communities in the impacted area of former uranium mine Senokos. The mine is one of the forty-nine former uranium mines in Bulgaria, reclaimed in the beginning of the 90s, but the reclamation was compromised due to surface erosion of the protective layers. The vegetation is typical for the region and uranium pollution has not caused any significant adverse affects on it. Adverse affects on soil bacterial communities are recorded only to their activity, but not to the abundance. Soil millipedes are in low density dominated by Pachyiulus cattarensis (Latzel 1884). The benthic community of Luda River is influenced by both uranium loaded sediments and infiltrate water from the mine.

Vertical Distribution of Bacterioplankton in Dolnoto Lake—Seven Rila Lakes

ABSTRACT Temporal and vertical distribution of bacterial abundance (BA) was studied in the high mountain Dolnoto Lake (max depth 11.0 m), the lake at lowest elevation (2 095 m a.s.l.) from the system of the Seven Rila Lakes. The research was performed in July and September, both in 2006 and 2007. Bacteriological analysis was completed with measurements of selected physicochemical parameters such as temperature, oxygen, pH, and inorganic nitrogen and phosphorous concentrations. BA was higher in 2007 (on average 93.92 x 105 cell ml−1), especially in Julq (121.56 x 105 cell ml−1) than in 2006 (on average 17.48 x 105 cell ml−1). The vertical distribution of bacteria depended on temperature, pH and PO4-P, and the most abundant was the subsurface layer of water column in most of the sampling occasions. Bacterial communities from the different vertical layers had different metabolic profiles (EcoPlates Biolog). The lowest physiological diversity was recoded for the subsurface bacterial community.

Mediterranean Land Use and Land Cover Classification Assessment Using High Spatial Resolution Data

Landscape fragmentation is noticeably practiced in Mediterranean regions and imposes substantial complications in several satellite image classification methods. To some extent, high spatial resolution data were able to overcome such complications. For better classification performances in Land Use Land Cover (LULC) mapping, the current research adopts different classification methods comparison for LULC mapping using Sentinel-2 satellite as a source of high spatial resolution. Both of pixel-based and an object-based classification algorithms were assessed; the pixel-based approach employs Maximum Likelihood (ML), Artificial Neural Network (ANN) algorithms, Support Vector Machine (SVM), and, the object-based classification uses the Nearest Neighbour (NN) classifier. Stratified Masking Process (SMP) that integrates a ranking process within the classes based on spectral fluctuation of the sum of the training and testing sites was implemented. An analysis of the overall and individual accuracy of the classification results of all four methods reveals that the SVM classifier was the most efficient overall by distinguishing most of the classes with the highest accuracy. NN succeeded to deal with artificial surface classes in general while agriculture area classes, and forest and semi-natural area classes were segregated successfully with SVM. Furthermore, a comparative analysis indicates that the conventional classification method yielded better accuracy results than the SMP method overall with both classifiers used, ML and SVM.

Functional Diversity of Microorganisms in Heavy Metal-Polluted Soils

In this chapter, we summarize the results from studies designed to assess the impacts of heavy metal pollution on the physiology of soil microorganisms based on a variety of commercially available assays (Biolog and MicroResp) of community substrate use. The results and conclusions from these studies are contradictory, depending on the metal concentrations and speciation, local environmental characteristics, and finally the different interpretations by the authors of the actual levels of pollution. In general, low and moderate levels (according to the Nemerow index) of metal pollution do not affect carbon use ability and functional diversity of the impacted microbial communities, as opposed to high metal pollution levels where significant adverse effects are recorded as functional responses of microbial communities to metal stress. Microbial functional responses to metal stress were observed as reduced catabolic activity and functional diversity, preferential community shifts from one carbon substrate use to another, and/or increased pollution-induced community tolerance. Finally, the microbial responses are summarized in the context of the modifying effects of the local environment on metal toxicity.

Soil Bacterial Abundance and Diversity of Uranium Impacted Area in North Western Pirin Mountain

ABSTRACT The former uranium mine Senocos (Blagoevgrad district, Bulgaria) has been exploited until 1991 when it was closed and later (1994–1997) reclaimed. Recently, the reclamation is compromised due to the erosion of protective layers and the mine wastes continue to affect the area resulting in increased radiation and uranium concentration in mine area. Soil bacterial abundance in more radioactive environment of mine remains unchanged (on average ((8.52±5.1) x 108 cell g−1) compared to the control ((8.76±3.5) x 108 cell g−1) in contrast to their dehydrogenase activity which decreases more than two times. Physiological and species diversity of bacterial mine communities are also affected by the pollution as it is expressed by lower values of Shannon indices compared to the control communities. All carbon sources in BIOLOG assay except 2-hydroxy benzoic acid are available for control bacterial communities and the number of unavailable carbon sources increases linearly with increasing the concentration of uranium in soil. The high levels of radionuclide pollution decrease the biodiversity to 3–4 species (66% of total) in the most polluted point of the mine territory (SPS(5)).

Non-woven Textile Materials from Waste Fibers for Cleanup of Waters Polluted with Petroleum and Oil Products

The aim of this work was to investigate the possibility of using non-woven materials (NWM) from waste fibers for oil spill cleanup and their subsequent recovery. Manufacture of textile and readymade products generates a significant amount of solid waste. A major part of it is deposited in landfills or disposed of uncontrollably. This slowly degradable waste causes environmental problems. In the present study are used two types of NWM obtained by methods where waste fibers are utilized. Thus, real textile products are produced (blankets) with which spills are covered and removed by adsorption. These products are produced by two methods: the strengthening of the covering from recovered fibers is made by entanglement when needles of special design pass through layers (needle-punching) or by stitching with thread (technology Maliwatt). Regardless of the random nature of the fiber mixture, the investigated products are good adsorbents of petroleum products. The nature of their structure (a significant void volume and developed surface) leads to a rapid recovery of the spilled petroleum products without sinking of the fiber layer for the sampled times. The used NWM can be burned under special conditions.

Understanding of the Geomorphological Elements in Discrimination of Typical Mediterranean Land Cover Types

Quantification of geomorphometric features is the keystone concern of the current study. The quantification was based on the statistical approach in term of multivariate analysis of local topographic features. The implemented algorithm utilizes the Digital Elevation Model (DEM) to categorize and extract the geomorphometric features embedded in the topographic dataset. The morphological settings were exercised on the central pixel of 3x3 per-defined convolution kernel to evaluate the surrounding pixels under the right directional pour point model (D8) of the azimuth viewpoints. Realization of unsupervised classification algorithm in term of Iterative Self-Organizing Data Analysis Technique (ISODATA) was carried out on ASTER GDEM within the boundary of the designated study area to distinguish 10 morphometric classes. The morphometric classes expressed spatial distribution variation in the study area. The adopted methodology is successful to appreciate the spatial distribution of the geomorphometric features under investigation. The conducted results verified the superimposition of the delineated geomorphometric elements over a given remote sensing imagery to be further analyzed. Robust relationship between different Land Cover types and the geomorphological elements was established in the context of the study area. The domination and the relative association of different Land Cover types in corresponding to its geomorphological elements were demonstrated.