Search

Methemoglobinemia is rare. It is classified into two types: congenital methemoglobinemia and acquired methemoglobinemia. Methemoglobin is incapable of binding oxygen, leading to complications such as cyanosis, dyspnea, headache, and heart failure. In the present case, a 35-year-old man with congenital methemoglobinemia underwent general anesthesia for thyroidectomy. The patient was diagnosed with hemoglobin M at 7 years of age. Ventilation was performed with FiO2 1.0. Arterial blood gas analysis showed that the pH was 7.4, PaO2 439 mmHg, PaCO2 40.5 mmHg, oxyhemoglobin level of 83.2%, and methemoglobin level of 15.5%. The patient had a stable course, although cyanosis was observed during surgery.

A technique for algal-bloom detection in European waters is described, based on standard chlorophyll a concentration (Chl) data from two ocean-colour sensors, the Moderate Resolution Imaging Spectroradiometer (MODIS) and Medium Resolution Imaging Spectrometer (MERIS). Comparison of the two data sources shows good agreement in case 1 waters, whereas the difference is significant in coastal waters including turbid areas. A relationship between the water-leaving reflectance at 667 nm and Chl for case 1 waters was used to eliminate pixels where Chl retrieval is contaminated by backscatter from inorganic suspended matter. Daily Chl data are compared to a predefined threshold map to determine whether an algal bloom has occurred. In this study, a threshold map was defined as the 90th percentile of previous years' data to take account of regional differences in typical Chl levels, with separate maps for each sensor to take account of sensor-specific bias. The algal-bloom detection processing chain is described, and example results are presented.

This paper presents results obtained with MIRO&CO-3D, a biogeochemical model dedicated to the study of eutrophication and applied to the Channel and Southern Bight of the North Sea (48.5°N–52.5°N). The model results from coupling of the COHERENS-3D hydrodynamic model and the biogeochemical model MIRO, which was previously calibrated in a multi-box implementation. MIRO&CO-3D is run to simulate the annual cycle of inorganic and organic carbon and nutrients (nitrogen, phosphorus and silica), phytoplankton (diatoms, nanoflagellates and Phaeocystis), bacteria and zooplankton (microzooplankton and copepods) with realistic forcing (meteorological conditions and river loads) for the period 1991–2003. Model validation is first shown by comparing time series of model concentrations of nutrients, chlorophyll a, diatom and Phaeocystis with in situ data from station 330 (51°26.00'N, 2°48.50'E) located in the centre of the Belgian coastal zone. This comparison shows the model's ability to represent the seasonal dynamics of nutrients and phytoplankton in Belgian waters. However the model fails to simulate correctly the dissolved silica cycle, especially during the beginning of spring, due to the late onset (in the model) of the early spring diatom bloom. As a general trend the chlorophyll a spring maximum is underestimated in simulations. A comparison between the seasonal average of surface winter nutrients and spring chlorophyll a concentrations simulated with in situ data for different stations is used to assess the accuracy of the simulated spatial distribution. At a seasonal scale, the spatial distribution of surface winter nutrients is in general well reproduced by the model with nevertheless a small overestimation for a few stations close to the Rhine/Meuse mouth and a tendency to underestimation in the coastal zone from Belgium to France. PO4 was simulated best; silica was simulated with less success. Spring chlorophyll a concentration is in general underestimated by the model. The accuracy of the simulated phytoplankton spatial distribution is further evaluated by comparing simulated surface chlorophyll a with that derived from the satellite sensor MERIS for the year 2003. Reasonable agreement is found between simulated and satellite-derived regions of high chlorophyll a with nevertheless discrepancies close to the boundaries.

Optical remote sensing data is now being used systematically for marine ecosystem applications, such as the forcing of biological models and the operational detection of harmful algae blooms. However, applications are hampered by the incompleteness of imagery and by some quality problems. The Data Interpolating Empirical Orthogonal Functions methodology (DINEOF) allows calculation of missing data in geophysical datasets without requiring a priori knowledge about statistics of the full dataset and has previously been applied to SST reconstructions. This study demonstrates the reconstruction of complete space–time information for 4 years of surface chlorophyll a (CHL), total suspended matter (TSM) and sea surface temperature (SST) over the Southern North Sea (SNS) and English Channel (EC). Optimal reconstructions were obtained when synthesising the original signal into 8 modes for MERIS CHL and into 18 modes for MERIS TSM. Despite the very high proportion of missing data (70%), the variability of original signals explained by the EOF synthesis reached 93.5% for CHL and 97.2% for TSM. For the MODIS TSM dataset, 97.5% of the original variability of the signal was synthesised into 14 modes. The MODIS SST dataset could be synthesised into 13 modes explaining 98% of the input signal variability. Validation of the method is achieved for 3 dates below 2 artificial clouds, by comparing reconstructed data with excluded input information. Complete weekly and monthly averaged climatologies, suitable for use with ecosystem models, were derived from regular daily reconstructions. Error maps associated with every reconstruction were produced according to Beckers et al. (2006). Embedded in this error calculation scheme, a methodology was implemented to produce maps of outliers, allowing identification of unusual or suspicious data points compared to the global dynamics of the dataset. Various algorithm artefacts were associated with high values in the outlier maps (undetected cloud edges, haze areas, contrails, and cloud shadows). With the production of outlier maps, the data reconstruction technique becomes also a very efficient tool for quality control of optical remote sensing data and for change detection within large databases.

Optical remote sensing in the North Sea is reviewed with a focus on applications supporting environmental management. Optical remote sensing provides estimates of Chlorophyll a, total suspended matter and diffuse attenuation coefficient and related parameters. These are used for harmful algal bloom detection, eutrophication assessment, ecosystem and sediment transport modeling, and estimation of air-sea carbon fluxes.

Theory and seaborne measurements are presented for the near infrared (NIR: 700-900 nm) water-leaving reflectance in turbid waters. According to theory, the shape of the NIR spectrum is determined largely by pure water absorption and is thus almost invariant. A ‘‘similarity’’ NIR reflectance spectrum is defined by normalization at 780 nm. This spectrum is calculated from seaborne reflectance measurements and is compared with that derived from laboratory water absorption measurements. Factors influencing the shape of the similarity spectrum are analyzed theoretically and by radiative transfer simulations. These simulations show that the similarity spectrum is valid for waters ranging from moderately turbid (e.g., water-leaving reflectance at 780 nm of order 10-4 or total suspended matter concentration of order 0.3 g m-3) to extremely turbid (e.g., reflectance at 780 nm of order 10-1 or total suspended matter of order 200 g m-3). Measurement uncertainties are analyzed, and the air-sea interface correction is shown to be critical for low reflectances. Applications of the NIR similarity spectrum to atmospheric correction of ocean color data and to the quality control of seaborne, airborne, and spaceborne reflectance measurements in turbid waters are outlined.

The color of the sea is determined by the contents of the water, especially the concentrations of suspended particulate matter (SPM), phytoplankton pigments such as chlorophyll (CHL) and colored dissolved organic matter (CDOM). Reversely, optical sensors that measure the water-leaving reflectance spectra allow us to calculate the desired concentration products. In this paper, a method is introduced that is valid for both case 1 and 2 waters. To this end, model is fitted to reflectance spectra, using simulated annealing for optimizing the mean square of the reflectance over all spectra.