Anthropogenic- and natural sources of dust in peatland during the Anthropocene

As human impact have been increasing strongly over the last decades, it is crucial to distinguish human-induced dust sources from natural ones in order to define the boundary of a newly proposed epoch - the Anthropocene. Here, we track anthropogenic signatures and natural geochemical anomalies in the Mukhrino peatland, Western Siberia. Human activity was recorded there from cal AD 1958 (±6). Anthropogenic spheroidal aluminosilicates clearly identify the beginning of industrial development and are proposed as a new indicator of the Anthropocene. In cal AD 1963 (±5), greatly elevated dust deposition and an increase in REE serve to show that the geochemistry of elements in the peat can be evidence of nuclear weapon testing; such constituted an enormous force blowing soil dust into the atmosphere. Among the natural dust sources, minor signals of dryness and of the Tunguska cosmic body (TCB) impact were noted. The TCB impact was indirectly confirmed by an unusual occurrence of mullite in the pea

MERCURY ADSORPTION BY ARTHOBACTER GLOBIFORMIS AND SPIRULINA PLATENSIS

Abstract. The increasing contamination of soil, sediment, and water with heavy metals by natural and industrial processes is a worldwide problem. Many bacteria and microalgae have demonstrated ability to absorb toxic elements. To study mercury biosorption by bacteria Arthrobacter globiformis and microalga Spirulina platensis neutron activation analysis (NAA) was applied. The process of mercury biosorption by these media was described by Freundlich and Langmuir-Freundlich Model. Both microorganisms showed a great potential to be used as biosorbing agents for mercury removal from the environment

SYNTHESIS OF GOLD NANOPARTICLES BY BLUE-GREEN ALGAE Spirulina platensis

Kalabegishvili T. et al. E14-2012-31 Synthesis of Gold Nanoparticles by Blue-Green Algae Spirulina platensis The synthesis of gold nanoparticles by one of the many popular microorganisms Å blue-green algae Spirulina platensis was studied. The complex of optical and analytical methods was applied for investigation of experimental samples after exposure to chloroaurate (HAuCl4) solution at different doses and for different time intervals. To characterize formed gold nanoparticles UV-vis, TEM, SEM, EDAX, and XRD were used. It was shown that after 1.5Ä2 days of exposure the extracellular formation of nanoparticles of spherical form and the distribution peak within the interval of 20Ä30 nm took place. To determine gold concentrations in the Spirulina platensis biomass, neutron activation analysis (NAA) and atomic absorption spectrometry (AAS) were applied. The results obtained evidence that the concentration of gold accumulated by Spirulina biomass is rapidly growing in the beginning, followed by some increase for the next few days. The obtained substance of Spirulina biomass with gold nanoparticles may be used for medical, pharmaceutical, and technological purposes. The investigation has been performed a

Modelling spatial patterns of correlations between concentrations of heavy metals in mosses and atmospheric deposition in 2010 across Europe

Background: This paper aims to investigate the correlations between the concentrations of nine heavy metals in moss and atmospheric deposition within ecological land classes covering Europe. Additionally, it is examined to what extent the statistical relations are affected by the land use around the moss sampling sites. Based on moss data collected in 2010/2011 throughout Europe and data on total atmospheric deposition modelled by two chemical transport models (EMEP MSC-E, LOTOS-EUROS), correlation coefficients between concentrations of heavy metals in moss and in modelled atmospheric deposition were specified for spatial subsamples defined by ecological land classes of Europe (ELCE) as a spatial reference system. Linear discriminant analysis (LDA) and logistic regression (LR) were then used to separate moss sampling sites regarding their contribution to the strength of correlation considering the areal percentage of urban, agricultural and forestry land use around the sampling location. After verification LDA models by LR, LDA models were used to transform spatial information on the land use to maps of potential correlation levels, applicable for future network planning in the European Moss Survey. Results: Correlations between concentrations of heavy metals in moss and in modelled atmospheric deposition were found to be specific for elements and ELCE units. Land use around the sampling sites mainly influences the correlation level. Small radiuses around the sampling sites examined (5 km) are more relevant for Cd, Cu, Ni, and Zn, while the areal percentage of urban and agricultural land use within large radiuses (75–100 km) is more relevant for As, Cr, Hg, Pb, and V. Most valid LDA models pattern with error rates of < 40% were found for As, Cr, Cu, Hg, Pb, and V. Land use-dependent predictions of spatial patterns split up Europe into investigation areas revealing potentially high (= above-average) or low (= below-average) correlation coefficients. Conclusions: LDA is an eligible method identifying and ranking boundary conditions of correlations between atmospheric deposition and respective concentrations of heavy metals in moss and related mapping considering the influence of the land use around moss sampling sites

Modelling and mapping heavy metal and nitrogen concentrations in moss in 2010 throughout Europe by applying Random Forests models

Objective: This study explores the statistical relations between the concentration of nine heavy metals(HM) (arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb),vanadium (V), zinc (Zn)), and nitrogen (N) in moss and potential explanatory variables (predictors)which were then used for mapping spatial patterns across Europe. Based on moss specimens collected in 2010 throughout Europe, the statistical relation between a set of potential predictors (such as the atmospheric deposition calculated by use of two chemical transport models (CTM), distance from emission sources, density of different land uses, population density, elevation, precipitation, clay content of soils) and concentrations of HMs and nitrogen (N) in moss (response variables) were evaluated by the use of Random Forests (RF) and Classification and Regression Trees (CART). Four spatial scales were regarded: Europe as a whole, ecological land classes covering Europe, single countries participating in the European Moss Survey (EMS), and moss species at sampling sites. Spatial patterns were estimated by applying a series of RF models on data on potential predictors covering Europe. Statistical values and resulting maps were used to investigate to what extent the models are specific for countries, units of the Ecological Land Classification of Europe (ELCE), and moss species. Results: Land use, atmospheric deposition and distance to technical emission sources mainly influence the element concentration in moss. The explanatory power of calculated RF models varies according to elements measured in moss specimens, country, ecological land class, and moss species. Measured and predicted medians of element concentrations agree fairly well while minima and maxima show considerable differences. The European maps derived from the RF models provide smoothed surfaces of element concentrations (As, Cd, Cr, Cu, N, Ni, Pb, Hg, V, Zn), each explained by a multivariate RF model and verified by CART, and thereby more information than the dot maps depicting the spatial patterns of measured values. Conclusions: RF is an eligible method identifying and ranking boundary conditions of element concentrations in moss and related mapping including the influence of the environmental factors

Modelling spatial patterns of correlations between concentrations of heavy metals in mosses and atmospheric deposition in 2010 across Europe

BackgroundThis paper aims to investigate the correlations between the concentrations of nine heavy metals in moss and atmospheric deposition within ecological land classes covering Europe. Additionally, it is examined to what extent the statistical relations are affected by the land use around the moss sampling sites. Based on moss data collected in 2010/2011 throughout Europe and data on total atmospheric deposition modelled by two chemical transport models (EMEP MSC-E, LOTOS-EUROS), correlation coefficients between concentrations of heavy metals in moss and in modelled atmospheric deposition were specified for spatial subsamples defined by ecological land classes of Europe (ELCE) as a spatial reference system. Linear discriminant analysis (LDA) and logistic regression (LR) were then used to separate moss sampling sites regarding their contribution to the strength of correlation considering the areal percentage of urban, agricultural and forestry land use around the sampling location. After verification LDA models by LR, LDA models were used to transform spatial information on the land use to maps of potential correlation levels, applicable for future network planning in the European Moss Survey.ResultsCorrelations between concentrations of heavy metals in moss and in modelled atmospheric deposition were found to be specific for elements and ELCE units. Land use around the sampling sites mainly influences the correlation level. Small radiuses around the sampling sites examined (5km) are more relevant for Cd, Cu, Ni, and Zn, while the areal percentage of urban and agricultural land use within large radiuses (75-100km) is more relevant for As, Cr, Hg, Pb, and V. Most valid LDA models pattern with error rates of <40% were found for As, Cr, Cu, Hg, Pb, and V. Land use-dependent predictions of spatial patterns split up Europe into investigation areas revealing potentially high (=above-average) or low (=below-average) correlation coefficients.ConclusionsLDA is an eligible method identifying and ranking boundary conditions of correlations between atmospheric deposition and respective concentrations of heavy metals in moss and related mapping considering the influence of the land use around moss sampling sites