21 resultados para soil attribute data

em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo


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Biodiesel production has received considerable attention in the recent past as a nonpolluting fuel. However, this assertion has been based on its biodegradability and reduction in exhaust emissions. Assessments of water and soil biodiesel pollution are still limited. Spill simulation with biodiesel and their diesel blends in soils were carried out, aiming at analyzing their cytotoxic and genotoxic potentials. While the cytotoxicity observed may be related to diesel contaminants, the genotoxic and mutagenic effects can be ascribed to biodiesel pollutants. Thus, taking into account that our data stressed harmful effects on organisms exposed to biodiesel-polluted soils, the designation of this biofuel as an environmental-friendly fuel should be carefully reviewed to assure environmental quality. (C) 2011 Elsevier B.V. All rights reserved.

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The Amazonian lowlands include large patches of open vegetation which contrast sharply with the rainforest, and the origin of these patches has been debated. This study focuses on a large area of open vegetation in northern Brazil, where d13C and, in some instances, C/N analyses of the organic matter preserved in late Quaternary sediments were used to achieve floristic reconstructions over time. The main goal was to determine when the modern open vegetation started to develop in this area. The variability in d13C data derived from nine cores ranges from -32.2 to -19.6 parts per thousand, but with nearly 60% of data above -26.5 parts per thousand. The most enriched values were detected only in ecotone and open vegetated areas. The development of open vegetation communities was asynchronous, varying between estimated ages of 6400 and 3000 cal a BP. This suggests that the origin of the studied patches of open vegetation might be linked to sedimentary dynamics of a late Quaternary megafan system. As sedimentation ended, this vegetation type became established over the megafan surface. In addition, the data presented here show that the presence of C4 plants must be used carefully as a proxy to interpret dry paleoclimatic episodes in Amazonian areas. Copyright (c) 2012 John Wiley & Sons, Ltd.

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The flow of sediment from cropped land is the main pollutant of water sources in rural areas. Due to this fact, it is necessary to develop and implement technologies that will reduce water and sediment discharges. Accordingly, an experiment was conducted in the Department of Biosystems Engineering - ESALQ / USP, Piracicaba - SP with the objective to evaluate the effect of different soil cover (bean, grass and bare ground) and erosion control practices (wide base terraces and infiltration furrows in slopes (no practices to control erosion)) while measuring water losses in runoff. The statistical design adopted was randomized blocks in a 3x3 factorial scheme resulting in 9 treatments with 3 replicates (blocks). The period of rainfall data collection was December 6, 2007 to April 11, 2008. A 21.1 cm diameter rain gauge was installed in the experimental area. Terraces were the most efficient practices for reducing erosion losses in the treatments with infiltration furrows being better than the control treatment. Bean was more effective than grass in reducing erosion. Bare ground was the least efficient.

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Methane (CH4) emission from agricultural soils increases dramatically as a result of deleterious effect of soil disturbance and nitrogen fertilization on methanotrophic organisms; however, few studies have attempted to evaluate the potential of long-term conservation management systems to mitigate CH4 emissions in tropical and subtropical soils. This study aimed to evaluate the long-term effect (>19 years) of no-till grass- and legume-based cropping systems on annual soil CH4 fluxes in a formerly degraded Acrisol in Southern Brazil. Air sampling was carried out using static chambers and CH4 analysis by gas chromatography. Analysis of historical data set of the experiment evidenced a remarkable effect of high C- and N-input cropping systems on the improvement of biological, chemical, and physical characteristics of this no-tilled soil. Soil CH4 fluxes, which represent a net balance between consumption (-) and production (+) of CH4 in soil, varied from -40 +/- 2 to +62 +/- 78 mu g C m(-2) h(-1). Mean weighted contents of ammonium (NH4+-N) and dissolved organic carbon (DOC) in soil had a positive relationship with accumulated soil CH4 fluxes in the post-management period (r(2) = 0.95, p = 0.05), suggesting an additive effect of these nutrients in suppressing CH4 oxidation and stimulating methanogenesis, respectively, in legume-based cropping systems with high biomass input. Annual CH4 fluxes ranged from -50 +/- 610 to +994 +/- 105 g C ha(-1), which were inversely related to annual biomass-C input (r(2) = 0.99, p = 0.003), with the exception of the cropping system containing pigeon pea, a summer legume that had the highest biologically fixed N input (>300 kg ha(-1) yr(-1)). Our results evidenced a small effect of conservation management systems on decreasing CH4 emissions from soil, despite their significant effect restoring soil quality. We hypothesized that soil CH4 uptake strength has been off-set by an injurious effect of biologically fixed N in legume-based cropping systems on soil methanotrophic microbiota, and by the methanogenesis increase as a result of the O-2 depletion in niches of high biological activity in the surface layer of the no-tillage soil. (C) 2012 Elsevier B.V. All rights reserved.

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The attributes describing a data set may often be arranged in meaningful subsets, each of which corresponds to a different aspect of the data. An unsupervised algorithm (SCAD) that simultaneously performs fuzzy clustering and aspects weighting was proposed in the literature. However, SCAD may fail and halt given certain conditions. To fix this problem, its steps are modified and then reordered to reduce the number of parameters required to be set by the user. In this paper we prove that each step of the resulting algorithm, named ASCAD, globally minimizes its cost-function with respect to the argument being optimized. The asymptotic analysis of ASCAD leads to a time complexity which is the same as that of fuzzy c-means. A hard version of the algorithm and a novel validity criterion that considers aspect weights in order to estimate the number of clusters are also described. The proposed method is assessed over several artificial and real data sets.

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Obtaining ecotoxicological data on pesticides in tropical regions is imperative for performing more realistic risk analysis, and avoidance tests have been proposed as a useful, fast and cost-effective tool. Therefore, the present study aimed to evaluate the avoidance behavior of Eisenia andrei to a formulated product, Vertimec(A (R)) 18 EC (a.i abamectin), in tests performed on a reference tropical artificial soil (TAS), to derive ecotoxicological data on tropical conditions, and a natural soil (NS), simulating crop field conditions. In TAS tests an adaptation of the substrate recommended by OECD and ISO protocols was used, with residues of coconut fiber as a source of organic matter. Concentrations of the pesticide on TAS test ranged from 0 to 7 mg abamectin/kg (dry weight-d.w.). In NS tests, earthworms were exposed to samples of soils sprayed in situ with: 0.9 L of Vertimec(A (R)) 18 EC/ha (RD); twice as much this dosage (2RD); and distilled water (Control), respectively, and to 2RD: control dilutions (12.5, 25, 50, 75%). All tests were performed under 25 +/- A 2A degrees C, to simulate tropical conditions, and a 12hL:12hD photoperiod. The organisms avoided contaminated TAS for an EC50,48h = 3.918 mg/kg soil d.w., LOEC = 1.75 mg/kg soil d.w. and NOEC = 0.85 mg/kg soil d.w. No significant avoidance response occurred for any NS test. Abamectin concentrations in NS were rather lower than EC50, 48h and LOEC determined in TAS tests. The results obtained contribute to overcome a lack of ecotoxicological data on pesticides under tropical conditions, but more tests with different soil invertebrates are needed to improve pesticides risk analysis.

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The taxonomic positions of two actinomycetes isolated from a hay meadow soil sample were determined using a polyphasic approach. The isolates had chemical and morphological properties typical of streptomycetes and formed a distinct 16S rRNA gene subclade together with the type strain Streptomyces drozdowiczii NRRL B-24297(T). DNA DNA relatedness studies showed that the three strains belonged to different genomic species. The organisms were also distinguished using a combination of phenotypic properties. On the basis of these data it is proposed that the isolates be assigned to the genus Streptomyces as Streptomyces brevispora sp. nov. and Streptomyces laculatispora sp. nov., with BK160(T) (=KACC 21093(T) =NCIMB 14702(T)) and BK166(T) (=KACC 20907(T) =NCIMB 14703(T)) as the respective type strains.

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In this analysis a 3.5 years data set of aerosol and precipitation chemistry, obtained in a remote site in Central Amazonia (Balbina, (1A degrees 55' S, 59A degrees 29' W, 174 m a.s.l.), about 200 km north of Manaus) is discussed. Aerosols were sampled using stacked filter units (SFU), which separate fine (d < 2.5 mu m) and coarse mode (2.5 mu m < d < 10.0 mu m) aerosol particles. Filters were analyzed for particulate mass (PM), Equivalent Black Carbon (BCE) and elemental composition by Particle Induced X-Ray Emission (PIXE). Rainwater samples were collected using a wet-only sampler and samples were analyzed for pH and ionic composition, which was determined using ionic chromatography (IC). Natural sources dominated the aerosol mass during the wet season, when it was predominantly of natural biogenic origin mostly in the coarse mode, which comprised up to 81% of PM10. Biogenic aerosol from both primary emissions and secondary organic aerosol dominates the fine mode in the wet season, with very low concentrations (average 2.2 mu g m(-3)). Soil dust was responsible for a minor fraction of the aerosol mass (less than 17%). Sudden increases in the concentration of elements as Al, Ti and Fe were also observed, both in fine and coarse mode (mostly during the April-may months), which we attribute to episodes of Saharan dust transport. During the dry periods, a significant contribution to the fine aerosols loading was observed, due to the large-scale transport of smoke from biomass burning in other portions of the Amazon basin. This contribution is associated with the enhancement of the concentration of S, K, Zn and BCE. Chlorine, which is commonly associated to sea salt and also to biomass burning emissions, presented higher concentration not only during the dry season but also for the April-June months, due to the establishment of more favorable meteorological conditions to the transport of Atlantic air masses to Central Amazonia. The chemical composition of rainwater was similar to those ones observed in other remote sites in tropical forests. The volume-weighted mean (VWM) pH was 4.90. The most important contribution to acidity was from weak organic acids. The organic acidity was predominantly associated with the presence of acetic acid instead of formic acid, which is more often observed in pristine tropical areas. Wet deposition rates for major species did not differ significantly between dry and wet season, except for NH4+, citrate and acetate, which had smaller deposition rates during dry season. While biomass burning emissions were clearly identified in the aerosol component, it did not present a clear signature in rainwater. The biogenic component and the long-range transport of sea salt were observed both in aerosols and rainwater composition. The results shown here indicate that in Central Amazonia it is still possible to observe quite pristine atmospheric conditions, relatively free of anthropogenic influences.

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Landfarm soils are employed in industrial and petrochemical residue bioremediation. This process induces selective pressure directed towards microorganisms capable of degrading toxic compounds. Detailed description of taxa in these environments is difficult due to a lack of knowledge of culture conditions required for unknown microorganisms. A metagenomic approach permits identification of organisms without the need for culture. However, a DNA extraction step is first required, which can bias taxonomic representativeness and interfere with cloning steps by extracting interference substances. We developed a simplified DNA extraction procedure coupled with metagenomic DNA amplification in an effort to overcome these limitations. The amplified sequences were used to generate a metagenomic data set and the taxonomic and functional representativeness were evaluated in comparison with a data set built with DNA extracted by conventional methods. The simplified and optimized method of RAPD to access metagenomic information provides better representativeness of the taxonomical and metabolic aspects of the environmental samples.

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Multi-element analysis of honey samples was carried out with the aim of developing a reliable method of tracing the origin of honey. Forty-two chemical elements were determined (Al, Cu, Pb, Zn, Mn, Cd, Tl, Co, Ni, Rb, Ba, Be, Bi, U, V, Fe, Pt, Pd, Te, Hf, Mo, Sn, Sb, P, La, Mg, I, Sm, Tb, Dy, Sd, Th, Pr, Nd, Tm, Yb, Lu, Gd, Ho, Er, Ce, Cr) by inductively coupled plasma mass spectrometry (ICP-MS). Then, three machine learning tools for classification and two for attribute selection were applied in order to prove that it is possible to use data mining tools to find the region where honey originated. Our results clearly demonstrate the potential of Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Random Forest (RF) chemometric tools for honey origin identification. Moreover, the selection tools allowed a reduction from 42 trace element concentrations to only 5. (C) 2012 Elsevier Ltd. All rights reserved.

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The aim of this study was to characterize and compare the spectral behavior of different soil classes obtained by orbital and terrestrial sensors. For this, an area of 184 ha in Rafard (SP) Brazil was staked on a regular grid of 100x100 m and soil samples were collected and georeferenced. After that, soil spectral curves were obtained with IRIS sensor and the sample points were overlaid at Landsat and ASTER images for spectral data collection. The soil samples were classified and mean soil curves for all sensors were generated by soil classes. The soil classes were differentiated by texture, organic matter and total iron for all sensors studied, the orbital sensors despite the lower spectral resolution, maintained the characteristics of the soil and the curves of reflectance intensity.

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The taxonomic positions of two thermophilic actinomycetes isolated from an arid Australian soil sample were established based on an investigation using a polyphasic taxonomic approach. The organisms had chemical and morphological properties typical of members of the genus Amycolatopsis and formed distinct phyletic lines in the Amycolatopsis methanolica 16S rRNA subclade. The two organisms were distinguished from one another and from the type strains of related species of the genus Amycolatopsis using a range of phenotypic properties. Based on the combined genotypic and phenotypic data, it is proposed that the two isolates be classified in the genus Amycolatopsis as Amycolatopsis thermophila sp. nov. (type strain GY088(T)=NCIMB 14699(T)=NRRL B-24836(T)) and Amycolatopsis viridis sp. nov. (type strain GY115(T)=NCIMB 14700(T)= NRRL B-24837(T)).

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In this paper, we propose nonlinear elliptical models for correlated data with heteroscedastic and/or autoregressive structures. Our aim is to extend the models proposed by Russo et al. [22] by considering a more sophisticated scale structure to deal with variations in data dispersion and/or a possible autocorrelation among measurements taken throughout the same experimental unit. Moreover, to avoid the possible influence of outlying observations or to take into account the non-normal symmetric tails of the data, we assume elliptical contours for the joint distribution of random effects and errors, which allows us to attribute different weights to the observations. We propose an iterative algorithm to obtain the maximum-likelihood estimates for the parameters and derive the local influence curvatures for some specific perturbation schemes. The motivation for this work comes from a pharmacokinetic indomethacin data set, which was analysed previously by Bocheng and Xuping [1] under normality.

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This study aims to compare and validate two soil-vegetation-atmosphere-transfer (SVAT) schemes: TERRA-ML and the Community Land Model (CLM). Both SVAT schemes are run in standalone mode (decoupled from an atmospheric model) and forced with meteorological in-situ measurements obtained at several tropical African sites. Model performance is quantified by comparing simulated sensible and latent heat fluxes with eddy-covariance measurements. Our analysis indicates that the Community Land Model corresponds more closely to the micrometeorological observations, reflecting the advantages of the higher model complexity and physical realism. Deficiencies in TERRA-ML are addressed and its performance is improved: (1) adjusting input data (root depth) to region-specific values (tropical evergreen forest) resolves dry-season underestimation of evapotranspiration; (2) adjusting the leaf area index and albedo (depending on hard-coded model constants) resolves overestimations of both latent and sensible heat fluxes; and (3) an unrealistic flux partitioning caused by overestimated superficial water contents is reduced by adjusting the hydraulic conductivity parameterization. CLM is by default more versatile in its global application on different vegetation types and climates. On the other hand, with its lower degree of complexity, TERRA-ML is much less computationally demanding, which leads to faster calculation times in a coupled climate simulation.

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The taxonomic positions of three streptomycetes isolated from a soil sample from a hay meadow were determined using a polyphasic approach. The isolates had chemical and morphological properties typical of the genus Streptomyces and, in phylogenetic analyses based on 16S rRNA gene sequences, formed a distinct subclade that was most closely related to the Streptomyces prasinus subclade. DNA-DNA relatedness studies showed that the novel strains belonged to three different genomic species. The novel strains could be distinguished from one another and from the type strains of the species classified in the S. prasinus subclade using a combination of genotypic and phenotypic properties. On the basis of these data, it is proposed that the novel strains be assigned to the genus Streptomyces as Streptomyces herbaceus sp. nov., Streptomyces incanus sp. nov. and Streptomyces pratens sp. nov., with BK119(T) (=KACC 21001(T) =CGMCC 4.5797(T)), BK128(T) (=KACC 21002(T) =CGMCC 4.5799(T)) and BK138(T) (=KACC 20904(T) =CGMCC 4.5800(T)) as the respective type strains.