960 resultados para sampling spatial location


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Research studies on the association between exposures to air contaminants and disease frequently use worn dosimeters to measure the concentration of the contaminant of interest. But investigation of exposure determinants requires additional knowledge beyond concentration, i.e., knowledge about personal activity such as whether the exposure occurred in a building or outdoors. Current studies frequently depend upon manual activity logging to record location. This study's purpose was to evaluate the use of a worn data logger recording three environmental parameters—temperature, humidity, and light intensity—as well as time of day, to determine indoor or outdoor location, with an ultimate aim of eliminating the need to manually log location or at least providing a method to verify such logs. For this study, data collection was limited to a single geographical area (Houston, Texas metropolitan area) during a single season (winter) using a HOBO H8 four-channel data logger. Data for development of a Location Model were collected using the logger for deliberate sampling of programmed activities in outdoor, building, and vehicle locations at various times of day. The Model was developed by analyzing the distributions of environmental parameters by location and time to establish a prioritized set of cut points for assessing locations. The final Model consisted of four "processors" that varied these priorities and cut points. Data to evaluate the Model were collected by wearing the logger during "typical days" while maintaining a location log. The Model was tested by feeding the typical day data into each processor and generating assessed locations for each record. These assessed locations were then compared with true locations recorded in the manual log to determine accurate versus erroneous assessments. The utility of each processor was evaluated by calculating overall error rates across all times of day, and calculating individual error rates by time of day. Unfortunately, the error rates were large, such that there would be no benefit in using the Model. Another analysis in which assessed locations were classified as either indoor (including both building and vehicle) or outdoor yielded slightly lower error rates that still precluded any benefit of the Model's use.^

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In geographical epidemiology, maps of disease rates and disease risk provide a spatial perspective for researching disease etiology. For rare diseases or when the population base is small, the rate and risk estimates may be unstable. Empirical Bayesian (EB) methods have been used to spatially smooth the estimates by permitting an area estimate to "borrow strength" from its neighbors. Such EB methods include the use of a Gamma model, of a James-Stein estimator, and of a conditional autoregressive (CAR) process. A fully Bayesian analysis of the CAR process is proposed. One advantage of this fully Bayesian analysis is that it can be implemented simply by using repeated sampling from the posterior densities. Use of a Markov chain Monte Carlo technique such as Gibbs sampler was not necessary. Direct resampling from the posterior densities provides exact small sample inferences instead of the approximate asymptotic analyses of maximum likelihood methods (Clayton & Kaldor, 1987). Further, the proposed CAR model provides for covariates to be included in the model. A simulation demonstrates the effect of sample size on the fully Bayesian analysis of the CAR process. The methods are applied to lip cancer data from Scotland, and the results are compared. ^

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In-situ Fe isotope measurements have been carried out to estimate the impact of the hydrothermal metamorphic overprint on the Fe isotopic composition of Fe-Ti-oxides and Fe-sulfides of the different lithologies of the drilled rocks from IODP Hole 1256D (eastern equatorial Pacific; 15 Ma crust formed at the East Pacific Rise). Most igneous rocks normally have a very restricted range in their 56Fe/54Fe ratio. In contrast, Fe isotope compositions of hot fluids (> 300 °C) from mid-ocean-ridge spreading centers define a narrow range that is shifted to lower delta 56Fe values by 0.2 per mil - 0.5 per mil as compared to igneous rocks. Therefore, it is expected that mineral phases that contain large amounts of Fe are especially affected by the interaction with a fluid that fractionates Fe isotopes during exsolution/precipitation of those minerals. We have used a femtosecond UV-Laser ablation system to determine mineral 56Fe/54Fe ratios of selected samples with a precision of < 0.1 per mil (2 sigma level) at micrometer-scale. We have found significant variations of the delta 56Fe (IRMM-014) values in the minerals between different samples as well as within samples and mineral grains. The overall observed scale of delta 56Fe (magnetite) in 1256D rocks ranges from - 0.12 to + 0.64 per mil, and of delta 56Fe (ilmenite) from - 0.77 to + 0.01 per mil. Pyrite in the lowermost sheeted dike section is clearly distinguishable from the other investigated lithological units, having positive delta 56Fe values between + 0.29 and + 0.56 per mil, whereas pyrite in the other samples has generally negative delta 56Fe values from - 1.10 to - 0.59 permil. One key observation is that the temperature dependent inter-mineral fractionations of Fe isotopes between magnetite and ilmenite are systematically shifted towards higher values when compared to theoretically expected values, while synthesized, well equilibrated magnetite-ilmenite pairs are compatible with the theoretical predictions. Theoretical considerations including beta-factors of different aqueous Fe-chlorides and Rayleigh-type fractionations in the presence of a hydrous, chlorine-bearing fluid can explain this observation. The disagreement between observed and theoretical equilibrium fractionation, the fact that magnetite, in contrast to ilmenite shows a slight downhole trend in the delta 56Fe values, and the observation of small scale heterogeneities within single mineral grains imply that a general re-equilibration of the magnetite-ilmenite pairs is overprinted by kinetic fractionation effects, caused by the interaction of magnetite/ilmenite with hydrothermal fluids penetrating the upper oceanic crust during cooling, or incomplete re-equilibration at low temperatures. Furthermore, the observation of significant small-scale variations in the 56Fe/54Fe ratios of single minerals in this study highlights the importance of high spatial-resolution-analyses of stable isotope ratios for further investigations.

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El presente estudio se realizó con la finalidad de modelizar la distribución espacial del carbón de la espiga del maíz causada por Sporisorium reilianum durante 2006 en el Estado de México y su visualización a través de la generación mapas de densidad. El muestreo se realizó en 100 parcelas georreferenciadas por cada localidad analizada. La incidencia de la enfermedad (porcentaje de plantas enfermas) se determinó al establecer cinco puntos parcela, en cada punto se contabilizaron 100 plantas. Se realizó el análisis geoestadístico para estimar el semivariograma experimental, una vez obtenido, se ajustó a un modelo teórico (esférico, exponencial o gaussiano) a través de los programas Variowin 2.2., su ajuste se validó a través de la validación cruzada. Posteriormente, se elaboraron mapas de agregación de la enfermedad con el método de interpolación geoestadística o krigeado. Los resultados indicaron que la enfermedad se presentó en 20 localidades de 19 municipios del Estado de México; todas las localidades presentaron un comportamiento espacial agregado de la enfermedad, 16 localidades se ajustaron al modelo esférico, dos al modelo exponencial y dos localidades se ajustaron al modelo gaussiano. En todos los modelos se lograron establecer mapas de agregación que permitirá adecuar las acciones de manejo en términos de puntos o sitios específicos.

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Suporting Information 1; Herbarium Corallina officinalis samples of the Natural History Museum (BM) analysed for the present study. Where the same NHM barcodes are provided for more than one sample, multiple samples were present under the same barcode in the herbarium. (-) indicates samples were not barcoded in the NHM (BM) system.

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Organochlorine compounds (OC) were determined in Arctic bivalves (Mya truncata, Serripes groenlan-dicus, Hiatella arctica and Chlamys islandica) from Svalbard with regard to differences in geographic location, species and variations related to their size and age. Higher chlorinated polychlorinated biphenyls (PCB 101-PCB 194), chlordanes and alpha-hexachlorocyclohexane (alpha-HCH) were consistently detected in the bivalves and PCBs dominated the OC load in the organisms. OC concentrations were highest in Mya truncata and the lowest in Serripes groenlandicus. Species-specific OC levels were likely related to differences in the species' food source, as indicated by the d13C results, rather than size and age. Higher OC concentrations were observed in bivalves from Kongsfjorden compared to the northern sampling locations Liefdefjorden and Sjuoyane. The spatial differences might be related to different water masses influencing Kongsfjorden (Atlantic) and the northern locations (Arctic), with differing phytoplankton bloom situations.

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The first data set contains the mean and cofficient of variation (standard deviation divided by mean) of a multi-frequency indicator I derived from ER60 acoustic information collected at five frequencies (18, 38, 70, 120, and 200 kHz) in the Bay of Biscay in May of the years 2006, 2008, 2009 and 2010 (Pelgas surveys). The multi-frequency indicator was first calculated per voxel (20 m long × 5 m deep sampling unit) and then averaged on a spatial grid (approx. 20 nm × 20 nm) for five 5-m depth layers in the surface waters (10-15m, 15-20m, 20-25m, 25-30m below sea surface); there are missing values in particular in the shallowest layer. The second data set provides for each grid cell and depth layer the proportion of voxels for which the multi-frequency indicator I was indicative of a certain group of organisms. For this the following interpretation was used: I < 0.39 swim bladder fish or large gas bubbles, I = 0.39-0.58 small resonant bubbles present in gas bearing organisms such as larval fish and phytoplankton, I = 0.7-0.8 fluidlike zooplankton such as copepods and euphausiids, and I > 0.8 mackerel. These proportions can be interpreted as a relative abundance index for each of the four organism groups.

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This data set comprises a time series of aboveground community plant biomass (Sown plant community, Weed plant community, Dead plant material, and Unidentified plant material; all measured in biomass as dry weight) and species-specific biomass from the sown species of the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. Aboveground community biomass was harvested twice a year just prior to mowing (during peak standing biomass twice a year, generally in May and August; in 2002 only once in September) on all experimental plots of the main experiment. This was done by clipping the vegetation at 3 cm above ground in up to four rectangles of 0.2 x 0.5 m per large plot. The location of these rectangles was assigned by random selection of new coordinates every year within the core area of the plots (i.e. the central 10 x 15 m). The positions of the rectangles within plots were identical for all plots. The harvested biomass was sorted into categories: individual species for the sown plant species, weed plant species (species not sown at the particular plot), detached dead plant material (i.e., dead plant material in the data file), and remaining plant material that could not be assigned to any category (i.e., unidentified plant material in the data file). All biomass was dried to constant weight (70°C, >= 48 h) and weighed. Sown plant community biomass was calculated as the sum of the biomass of the individual sown species. The data for individual samples and the mean over samples for the biomass measures on the community level are given. Overall, analyses of the community biomass data have identified species richness as well as functional group composition as important drivers of a positive biodiversity-productivity relationship.

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The surroundings of the Cortiou sewage are among the most polluted environments of the French Mediterranean Sea (Marseilles, France). So far, no studies have precisely quantified the impact of pollution on the development of organisms in this area.Methods: We used a fluctuating asymmetry (FA) measure of developmental instability (DI) to assess environmental stress in two species of radially symmetric sea urchins (Arbacia lixula and Paracentrotus lividus). For six sampling sites (Cortiou, Riou, Maire, East Maire, Mejean, and Niolon), levels of FA were calculated from continuous and discrete skeletal measures of ambulacral length, number of pore pairs and primary tubercles.Results: For both species, the most polluted sampling site, Cortiou, displayed the highest level of FA, while the Maire and East Maire sampling sites displayed the lowest levels. A. lixula revealed systematic differences in FA among sampling sites for all characters and P. lividus showed differences in FA for the number of primary tubercles.Conclusions: Statistical analyses of FA show a concordance between the spatial patterns of FA among sampling sites and the spatial distribution of sewage discharge pollutants in the Cortiou area. High developmental stress in these sampling sites is associated with exposure to high concentrations of heavy metals and many harmful organic substances contained in wastewater. FA estimated from structures with complex symmetry appears to be a fast and reliable tool to detect subtle differences in FA. Its use in biomonitoring programs for inferring anthropogenic and natural environmental stress is suggested.

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The overarching goal of the Yamal portion of the Greening of the Arctic project is to examine how the terrain and anthropogenic factors of reindeer herding and resource development combined with the climate variations on the Yamal Peninsula affect the spatial and temporal patterns of vegetation change and how these changes are in turn affecting traditional herding of the indigenous people of the region. The purpose of the expeditions was to collect groundobservations in support of remote sensing studies at four locations along a transect that traverses all the major bioclimate subzones of the Yamal Peninsula. This data report is a summary of information collected during the 2007 and 2008 expeditions. It includes all the information from the 2008 data report (Walker et al. 2008) plus new information collected at Kharasavey in Aug 2008. The locations included in this report are Nadym (northern taiga subzone), Laborovaya (southern tundra = subzone E of the Circumpolar Arctic Vegetation Map (CAVM), Vaskiny Dachi (southern typical tundra = subzone D), and Kharasavey (northern typical tundra = subzone C). Another expedition is planned for summer 2009 to the northernmost site at Belyy Ostrov (Arctic tundra = subzone B). Data are reported from 10 study sites - 2 at Nadym, 2 at Laborovaya, and 3 at Vaskiny Dachi and 3 at Kharasavey. The sites are representative of the zonal soils and vegetation, but also include variation related to substrate (clayey vs. sandy soils). Most of the information was collected along 5 transects at each sample site, 5 permanent vegetation study plots, and 1-2 soil pits at each site. The expedition also established soil and permafrost monitoring sites at each location. This data report includes: (1) background for the project, (2) general descriptions and photographs of each locality and sample site, (3) maps of the sites, study plots, and transects at each location, (4) summary of sampling methods used, (5) tabular summaries of the vegetation data (species lists, estimates of cover abundance for each species within vegetation plots, measured percent ground cover of species along transects, site factors for each study plot), (6) summaries of the Normalized Difference Vegetation Index (NDVI) and leaf area index (LAI) along each transect, (7) soil descriptions and photos of the soil pits at each study site, (8) summaries of thaw measurements along each transect, and (9) contact information for each of the participants. One of the primary objectives was to provide the Russian partners with full documentation of the methods so that Russian observers in future years could repeat the observations independently.

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This data set comprises a time series of aboveground community plant biomass (Sown plant community, Weed plant community, Dead plant material, and Unidentified plant material; all measured in biomass as dry weight) and species-specific biomass from the sown species of the dominance experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the dominance experiment, 206 grassland plots of 3.5 x 3.5 m were established from a pool of 9 species that can be dominant in semi-natural grassland communities of the study region. In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 3, 4, 6, and 9 species). Plots were maintained by bi-annual weeding and mowing. Aboveground community biomass was harvested twice a year, generally in May and August (in 2002 only once in September) on all experimental plots of the dominance experiment. This was done by clipping the vegetation at 3 cm above ground in two rectangles of 0.2 x 0.5 m per experimental plot. The location of these rectangles was assigned by random selection of new coordinates every year within the central area of the plots (excluding an outer edge of 50cm). The positions of the rectangles within plots were identical for all plots. The harvested biomass was sorted into categories: individual species for the sown plant species, weed plant species (species not sown at the particular plot), detached dead plant material, and remaining plant material that could not be assigned to any category. Biomass was dried to constant weight (70°C, >= 48 h) and weighed. Sown plant community biomass was calculated as the sum of the biomass of the individual sown species. The mean of both samples per plot and the individual measurements are provided in the data file. Overall, analyses of the community biomass data have identified species richness and the presence of particular species as an important driver of a positive biodiversity-productivity relationship.