Fragmentation of extracellular DNA by long-term exposure to radiation from uranium in aquatic environments

Persistent harmful scenarios associated with disposal of radioactive waste, high-background radiation areas and severe nuclear accidents are of great concern regarding consequences to both human health and the environment. Of particular concern is the extracellular DNA in aquatic environments contaminated by radiological substances. Strand breaks induced by radiation promote decrease in the transformation efficiency for extracellular DNA. The focus of this study is the quantification of DNA damage following long-term exposure (over one year) to low doses of natural uranium (an alpha particle emitter) to simulate natural conditions, since nothing is known about alpha radiation induced damage to extracellular DNA. A high-resolution Atomic Force Microscope was used to evaluate DNA fragments. Double-stranded plasmid pBS as a model for extracellular DNA was exposed to different amounts of natural uranium. It was demonstrated that low concentrations of U in water (50 to 150 ppm) produce appreciable numbers of double strand breaks, scaling with the square of the average doses. The importance of these findings for environment monitoring of radiological pollution is addressed.     

A Multiple Criteria Decision Analysis Model Based on ELECTRE TRI-C for Erosion Risk Assessment in Agricultural Areas

This paper deals with a real-world decision-aiding problem for zoning the risk of erosion, total suspended solids emissions, and ecological consequences of their transfers towards the streams. One of these consequences is the decrease of fishes into the streams in agricultural watersheds, because of the clogging of spawning areas. Given the multiple criteria nature of the problem, the originality of our research is to adapt a new decision-aiding sorting method, ELECTRE TRI-C, for identifying risk zones in rural areas, where measures must be taken. The developed model was applied in a small watershed (Low Normandy, France) where the objective was to assess the most appropriate intervention for protecting the reproduction habitat of the salmonid fishes. Agricultural parcels were evaluated on multiple criteria for grouping them into four risk categories, which are related to risk levels as well as priorities on the improvement works. The decision-aiding sorting model is co-constructed, within a constructive approach, through an interaction process between decision-aiding analysts, environmental experts, and local actors for improving transparency and communication on the results. This model is linked with a geographical information system (GIS) for assessing a set of criteria and the visualization of the farming parcels along with their type of intervention they should be submitted to best practices. The assignment results were validated by the environmental experts. These results have a strong impact on the agricultural practices of the farmers into the watersheds. The model proposed in this paper can be considered as a useful decision aid tool in any regions for implementing public agricultural and environmental policies for protecting the ecological areas.     

Biomonitoring of polychlorinated biphenyls (PCBs) in heavily polluted aquatic environment in different fish species

The distribution and concentrations of polychlorinated biphenyls (PCBs) were determined in fish species (European perch Perca fluviatilis, northern pike Esox lucius, pike perch Sander lucioperca, wels catfish Silirus glanus, common carp Cyprinus carpio, European eel Anguilla anguilla, freshwater bream Abramis brama, goldfish Carassius auratus, and roach Rutilus rutilus) in a heavily polluted water reservoir Zemplínska ?írava (Slovakia). The study performed at two different time points 5?years apart (2004 and 2009) revealed serious PCB contamination of fish muscle tissue and significant interspecies as well as tissue-specific differences in PCB uptake by fish. Total PCBs broadly correlated with the trophic position of individual fish species within a food chain (P??0.05). The study has shown that the kind of fish, its feeding habit, and specific conditions of the habitat are mutually interrelated factors that are responsible for significant variations in fish body burdens. A tendency to PCB biomagnification was also proved in some fish species of this water reservoir.     

Analysis of long-term water quality for effective river health monitoring in peri-urban landscapes—a case study of the Hawkesbury–Nepean river system in NSW, Australia

The Hawkesbury–Nepean River (HNR) system in South-Eastern Australia is the main source of water supply for the Sydney Metropolitan area and is one of the more complex river systems due to the influence of urbanisation and other activities in the peri-urban landscape through which it flows. The long-term monitoring of river water quality is likely to suffer from data gaps due to funding cuts, changes in priority and related reasons. Nevertheless, we need to assess river health based on the available information. In this study, we demonstrated how the Factor Analysis (FA), Hierarchical Agglomerative Cluster Analysis (HACA) and Trend Analysis (TA) can be applied to evaluate long-term historic data sets. Six water quality parameters, viz., temperature, chlorophyll-a, dissolved oxygen, oxides of nitrogen, suspended solids and reactive silicates, measured at weekly intervals between 1985 and 2008 at 12 monitoring stations located along the 300 km length of the HNR system were evaluated to understand the human and natural influences on the river system in a peri-urban landscape. The application of FA extracted three latent factors which explained more than 70 % of the total variance of the data and related to the ‘bio-geographical’, ‘natural’ and ‘nutrient pollutant’ dimensions of the HNR system. The bio-geographical and nutrient pollution factors more likely related to the direct influence of changes and activities of peri-urban natures and accounted for approximately 50 % of variability in water quality. The application of HACA indicated two major clusters representing clean and polluted zones of the river. On the spatial scale, one cluster was represented by the upper and lower sections of the river (clean zone) and accounted for approximately 158 km of the river. The other cluster was represented by the middle section (polluted zone) with a length of approximately 98 km. Trend Analysis indicated how the point sources influence river water quality on spatio-temporal scales, taking into account the various effects of nutrient and other pollutant loads from sewerage effluents, agriculture and other point and non-point sources along the river and major tributaries of the HNR. Over the past 26 years, water temperature has significantly increased while suspended solids have significantly decreased (p?<?0.05). The analysis of water quality data through FA, HACA and TA helped to characterise the key sections and cluster the key water quality variables of the HNR system. The insights gained from this study have the potential to improve the effectiveness of river health-monitoring programs in terms of cost, time and effort, particularly in a peri-urban context.     

Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

Understanding the spatial soil salinity aids farmers and researchers in identifying areas in the field where special management practices are required. Apparent electrical conductivity measured by electromagnetic induction instrument in a fairly quick manner has been widely used to estimate spatial soil salinity. However, methods used for this purpose are mostly a series of interpolation algorithms. In this study, sequential Gaussian simulation (SGS) and sequential Gaussian co-simulation (SGCS) algorithms were applied for assessing the prediction accuracy and uncertainty of soil salinity with apparent electrical conductivity as auxiliary variable. Results showed that the spatial patterns of soil salinity generated by SGS and SGCS algorithms showed consistency with the measured values. The profile distribution of soil salinity was characterized by increasing with depth with medium salinization (EC_e 4–8 dS/m) as the predominant salinization class. SGCS algorithm privileged SGS algorithm with smaller root mean square error according to the generated realizations. In addition, SGCS algorithm had larger proportions of true values falling within probability intervals and narrower range of probability intervals than SGS algorithm. We concluded that SGCS algorithm had better performance in modeling local uncertainty and propagating spatial uncertainty. The inclusion of auxiliary variable contributed to prediction capability and uncertainty modeling when using densely auxiliary variable as the covariate to predict the sparse target variable.     

A geographical approach to tracking Escherichia coli and other water quality constituents in a Texas coastal plains watershed

Diffuse sources of surface water pathogens and nutrients can be difficult to isolate in larger river basins. This study used a geographical or nested approach to isolate diffuse sources of Escherichia coli and other water quality constituents in a 145.7-km² river basin in south central Texas, USA. Average numbers of E. coli ranged from 49 to 64,000 colony forming units (CFU) per 100 mL depending upon season and stream flow over the 1-year sampling period. Nitrate-N concentrations ranged from 48 to 14,041 μg?L^?1 and orthophosphate-P from 27 to 2,721 μg?L^?1. High concentrations of nitrate-N, dissolved organic nitrogen, and orthophosphate-P were observed downstream of waste water treatment plants but E. coli values were higher in a watershed draining an older part of the city. Total urban land use explained between 56 and 72 % of the variance in mean annual E. coli values (p?<?0.05) in nine hydrologically disconnected creeks. Of the types of urban land use, commercial land use explained most of the variance in E. coli values in the fall and winter. Surface water sodium, alkalinity, and potassium concentrations in surface water were best described by the proportion of commercial land use in the watershed. Based on our nested approach in examining surface water, city officials are able to direct funding to specific areas of the basin in order to mitigate high surface water E. coli numbers and nutrient concentrations.     

Spatial patterns of heavy metals in soil under different geological structures and land uses for assessing metal enrichments

One hundred and thirty composite soil samples were collected from Hamedan county, Iran to characterize the spatial distribution and trace the sources of heavy metals including As, Cd, Co, Cr, Cu, Ni, Pb, V, Zn, and Fe. The multivariate gap statistical analysis was used; for interrelation of spatial patterns of pollution, the disjunctive kriging and geoenrichment factor (EF_G) techniques were applied. Heavy metals and soil properties were grouped using agglomerative hierarchical clustering and gap statistic. Principal component analysis was used for identification of the source of metals in a set of data. Geostatistics was used for the geospatial data processing. Based on the comparison between the original data and background values of the ten metals, the disjunctive kriging and EF_G techniques were used to quantify their geospatial patterns and assess the contamination levels of the heavy metals. The spatial distribution map combined with the statistical analysis showed that the main source of Cr, Co, Ni, Zn, Pb, and V in group A land use (agriculture, rocky, and urban) was geogenic; the origin of As, Cd, and Cu was industrial and agricultural activities (anthropogenic sources). In group B land use (rangeland and orchards), the origin of metals (Cr, Co, Ni, Zn, and V) was mainly controlled by natural factors and As, Cd, Cu, and Pb had been added by organic factors. In group C land use (water), the origin of most heavy metals is natural without anthropogenic sources. The Cd and As pollution was relatively more serious in different land use. The EF_G technique used confirmed the anthropogenic influence of heavy metal pollution. All metals showed concentrations substantially higher than their background values, suggesting anthropogenic pollution.     

Prediction of the solubility of zinc, copper, nickel, cadmium, and lead in metal-contaminated soils

Risk assessment of metal-contaminated soil depends on how precisely one can predict the solubility of metals in soils. Responses of plants and soil organisms to metal toxicity are explained by the variation in free metal ion activity in soil pore water. This study was undertaken to predict the free ion activity of Zn, Cu, Ni, Cd, and Pb in metal-contaminated soil as a function of pH, soil organic carbon, and extractable metal content. For this purpose, 21 surface soil samples (0–15 cm) were collected from agricultural lands of various locations receiving sewage sludge and industrial effluents for a long period. One soil sample was also collected from agricultural land which has been under intensive cropping and receiving irrigation through tube well water. Soil samples were varied widely in respect of physicochemical properties including metal content. Total Zn, Cu, Ni, Cd, and Pb in experimental soils were 2,015?±?3,373, 236?±?286, 103?±?192, 29.8?±?6.04, and 141?±?270 mg kg^?1, respectively. Free metal ion activity, viz., pZn²⁺, pCu²⁺, pNi²⁺, pCd²⁺, and pPb²⁺, as estimated by the Baker soil test was 9.37?±?1.89, 13.1?±?1.96, 12.8?±?1.89, 11.9?±?2.00, and 11.6?±?1.52, respectively. Free metal ion activity was predicted by pH-dependent Freundlich equation (solubility model) as a function of pH, organic carbon, and extractable metal. Results indicate that solubility model as a function of pH, Walkley–Black carbon (WBC), and ethylenediaminetetraacetic acid (EDTA)-extractable metals could explain the variation in pZn²⁺, pCu²⁺, pNi²⁺, pCd²⁺, and pPb²⁺ to the extent of 59, 56, 46, 52, and 51 %, respectively. Predictability of the solubility model based on pH, KMnO₄-oxidizable carbon, and diethylenetriaminepentaacetic acid-extractable or CaCl₂-extractable metal was inferior compared to that based on EDTA-extractable metals and WBC.     

Sand as a relevant fraction in geochemical studies in intertidal environments

Soil and sediment samples from several intertidal environment exposed to different types of contamination were studied to investigate the importance of grain size in relation to the capacity of the substrates to retain trace metals. The unfractionated samples (referred to as bulk samples) were separated into the following grain/size fractions: fine–coarse sand (2?0.100 mm), very fine sand (0.100?0.050 mm), silt (0.050?0.002 mm), and clay (0.002 mm). The sample into its fractions was carried out was in a glove box under high-purity N₂ atmosphere in order to minimize any alterations to the samples. The bulk samples were characterized in terms of physicochemical properties such as pH, redox potential, and grain size. The total organic carbon (TOC), total sulfur (S), iron (Fe) pyrite, Fe, and manganese (Mn), and trace metals lead (Pb), mercury (Hg), chromium (Cr), and nickel (Ni) were analyzed in the bulk samples and in each fraction. The sand fractions were also examined by scanning electron microscopy (SEM). Comparisons of the above parameters were made between fractions and between each fraction and the corresponding bulk sample. The fine–coarse sand fraction contained high levels of the primary elements of the geochemical processes that occur in marine sedimentary environments such as TOC, total Fe, Mn, and S. The net concentrations of these four elements were higher in the fine-coarse sand fraction than in the very fine sand fraction and were similar to the net concentrations in the silt and clay fractions. Detailed SEM analysis of the sand coarse fraction revealed the presence of Fe and aluminum oxyhydroxide coatings in the oxic layers, whereas the framboidal pyrites and coatings observed in the anoxic layers were Fe sulfides. The presence of the various coatings explains why the trace metal concentrations in the sand fine–coarse fraction were similar to those in the clay fraction and higher than those in the very fine sand fraction. The present results highlight the importance of the sand fraction, which is generally disregarded in geochemical and environmental studies of sedimentary layers.     

The LUCAS topsoil database and derived information on the regional variability of cropland topsoil properties in the European Union

Approximately 20,000 topsoil samples were collected in 25 European Union (EU) Member States (EU-27 except Bulgaria and Romania) with the aim to produce the first coherent pan-European physical and chemical topsoil database, which can serve as baseline information for an EU wide harmonized soil monitoring. The soil sampling was undertaken within the frame of the Land Use/Land Cover Area Frame Survey (LUCAS), a project to monitor changes in the management and character of the land surface of the EU. Soil samples have been analysed for basic soil properties, including particle size distribution, pH, organic carbon, carbonates, NPK, cation exchange capacity (CEC) and multispectral signatures. Preliminary studies show the outstanding potential of the dataset for enhancing the knowledge base on soils in the EU. The current paper provides an introduction to the LUCAS Topsoil 2009 project and provides an example of data applicability for cropland assessment by highlighting initial results for regional and national comparisons.