Characterization of microbial communities from coastal waters using microarrays

Molecular methods, including DNA probes, were used to identify and enumerate pathogenic Vibrio species in the Chesapeake Bay; our data indicated that Vibrio vulnificus exhibits seasonal fluctuations in number. Our work included a characterization of total microbial communities from the Bay; development of microarrays that identify and quantify the diversity of those communities; and observation of temporal changes in those communities. To identify members of the microbial community, we amplified the 16S rDNA gene from community DNA isolated from a biofilm sample collected from the Chesapeake Bay in February, 2000. The resultant 75 sequences were 95% or more similar to 7 species including two recently described Shewanella species, baltica and frigidimarina, that have not been previously isolated from the Chesapeake. When the genera of bacteria from biofilm after culturing are compared to those detected by subcloning amplified 16S fragments from community DNA, the cultured sample exhibited a strong bias. In oysters collected in February, the most common bacteria were previously unknown. Based on our 16S findings, we are developing microarrays to detect these and other microbial species in these estuarine communities. The microarrays will detect each species using four distinct loci, with the multiple loci serving as an internal control. The accuracy of the microarray will be measured using sentinel species such as Aeromonas species, Escherichia coli, and Vibrio vulnificus. Using microarrays, it should be possible to determine the annual fluctuations of bacterial species (culturable and non-culturable, pathogenic and non-pathogenic). The data may be applied to understanding patterns of environmental change; assessing the health of the Bay; and evaluating the risk of human illness associated with exposure to and ingestion of water and shellfish.     

Experimental verification for real-time environmental emergency response system: WSPEEDI by European tracer experiment

Japan Atomic Energy Research Institute has developed an emergency response system WSPEEDI to forecast long-range atmospheric dispersions of radionuclides discharged into the atmosphere. The latest version of WSPEEDI consists of an atmospheric dynamic model MM5 for calculating meteorological fields and a particle random-walk model for atmospheric dispersion. The performance of WSPEEDI was evaluated by data obtained from a field tracer experiment over Europe (ETEX) in this paper. The model validation was done with respect to the following points: (1) the dependence of model accuracy on the temporal and spatial resolutions of the meteorological fields and (2) the superiority of an atmospheric dynamic model over a mass-consistent wind model. Regarding (1), it was shown that the calculation accuracy of the new version with high temporal resolution was improved, especially at the edge of the plume. Moreover, although the increase in horizontal spatial resolution of the old version had no substantial effect on the model performance, increase in horizontal resolution of the new version contributed to the significant improvement of the calculation accuracy. These results showed that the dynamically calculated meteorological field with the spatial resolution of the meso-β–γ scale greatly improved calculation accuracy.     

Evaluation of a long-range lagrangian dispersion model with ETEX

Performance of a Lagrangian dispersion model was examined in connection with its dependency on the boundary layer modelling and the input data resolution. The European Tracer Experiment (ETEX) data were used as reference. According to the sensitivity analysis of the model performance, the long-range dispersion model with the sparse input data was not noticeably different from that with the finer resolution data. The assumption of the prescribed constant mixing depth did not largely degrade the prediction results as compared with the simulation results with the temporally changing boundary layer. It is, therefore, concluded that the model is practical, considering the limited input data in the operational mode. However, it was also pointed out that the parameterization for the horizontal and vertical diffusion processes used in the present model enhanced the growth of plume. The improvement of input data resolution in time and space caused further dispersion of tracer deterministically. These resulted in the underestimation of the maximum concentration and the unfocussed concentration distribution map although the mean concentration was predicted fairly well.