974 resultados para Chemical and biological parameters
Resumo:
Pingualuk Lake fills a deep crater in the Parc National des Pingualuit on the Ungava Peninsula (Nunavik, Canada) and is isolated from nearby surface waters. The main objectives of this study were to determine and compare the concentrations of two atmospherically derived contaminants, mercury and perfluorinated chemicals (PFCs), in the lake water column and fish of Pingualuk Lake and to assess the physical and biological factors influencing contaminant concentrations. Mercury concentrations in arctic char muscle tissue were comparable to those of char in other Arctic lakes, while the total amount of PFCs was below reported levels for remote lakes in the Arctic and elsewhere. Stable isotope and stomach content analyses were made to investigate the feeding ecology of the Pingualuk Lake arctic char population and indicated the possibility of multiple feeding groups. Genetics characteristics (MH and mtDNA) of fish from Pingualuk Lake revealed that this population is likely distinct from that of nearby Laflamme Lake. However, both arctic char populations exhibit differential variation of their allele families. Physical characteristics determined for Lake Pingualuk revealed that the water column was inversely stratified beneath the ice and extremely transparent to visible and ultraviolet radiation. The highest mercury concentrations (3- 6 pg/mL THg) occurred just beneath the ice surface in each lake. Pingualuk Lake, given its near pristine state and exceptional limnological features, may serve as a most valuable reference ecosystem for monitoring environmental stressors, such as contaminants, in the Arctic.
Resumo:
Geostrophic surface velocities can be derived from the gradients of the mean dynamic topography-the difference between the mean sea surface and the geoid. Therefore, independently observed mean dynamic topography data are valuable input parameters and constraints for ocean circulation models. For a successful fit to observational dynamic topography data, not only the mean dynamic topography on the particular ocean model grid is required, but also information about its inverse covariance matrix. The calculation of the mean dynamic topography from satellite-based gravity field models and altimetric sea surface height measurements, however, is not straightforward. For this purpose, we previously developed an integrated approach to combining these two different observation groups in a consistent way without using the common filter approaches (Becker et al. in J Geodyn 59(60):99-110, 2012, doi:10.1016/j.jog.2011.07.0069; Becker in Konsistente Kombination von Schwerefeld, Altimetrie und hydrographischen Daten zur Modellierung der dynamischen Ozeantopographie, 2012, http://nbn-resolving.de/nbn:de:hbz:5n-29199). Within this combination method, the full spectral range of the observations is considered. Further, it allows the direct determination of the normal equations (i.e., the inverse of the error covariance matrix) of the mean dynamic topography on arbitrary grids, which is one of the requirements for ocean data assimilation. In this paper, we report progress through selection and improved processing of altimetric data sets. We focus on the preprocessing steps of along-track altimetry data from Jason-1 and Envisat to obtain a mean sea surface profile. During this procedure, a rigorous variance propagation is accomplished, so that, for the first time, the full covariance matrix of the mean sea surface is available. The combination of the mean profile and a combined GRACE/GOCE gravity field model yields a mean dynamic topography model for the North Atlantic Ocean that is characterized by a defined set of assumptions. We show that including the geodetically derived mean dynamic topography with the full error structure in a 3D stationary inverse ocean model improves modeled oceanographic features over previous estimates.
