Oil spill problems and sustainable response strategies through new technologies

Crude oil and petroleum products are widespread water and soil pollutants resulting from marine and terrestrial spillages. International statistics of oil spill sizes for all incidents indicate that the majority of oil spills are small (less than 7 tonnes). The major accidents that happen in the oil industry contribute only a small fraction of the total oil which enters the environment. However, the nature of accidental releases is that they highly pollute small areas and have the potential to devastate the biota locally. There are several routes by which oil can get back to humans from accidental spills, e.g. through accumulation in fish and shellfish, through consumption of contaminated groundwater. Although advances have been made in the prevention of accidents, this does not apply in all countries, and by the random nature of oil spill events, total prevention is not feasible. Therefore, considerable world-wide effort has gone into strategies for minimising accidental spills and the design of new remedial technologies. This paper summarizes new knowledge as well as research and technology gaps essential for developing appropriate decision-making tools in actual spill scenarios. Since oil exploration is being driven into deeper waters and more remote, fragile environments, the risk of future accidents becomes much higher. The innovative safety and accident prevention approaches summarized in this paper are currently important for a range of stakeholders, including the oil industry, the scientific community and the public. Ultimately an integrated approach to prevention and remediation that accelerates an early warning protocol in the event of a spill would get the most appropriate technology selected and implemented as early as possible-the first few hours after a spill are crucial to the outcome of the remedial effort. A particular focus is made on bioremediation as environmentally harmless, cost-effective and relatively inexpensive technology. Greater penetration into the remedial technologies market depends on the harmonization of environment legislation and the application of modern laboratory techniques, e.g. ecogenomics, to improve the predictability of bioremediation

Structure of feeding for <i>Echinarachnius parma</i> and <i>Scaphechinus mirabilis</i> (Echinoidea, Clypeasteroida) in the Troitsa Bay, Japan Sea

Feeding of sand dollars Echinarachnius parma and Scaphechinus mirabilis (Clypeasteroida) in the Troitsa Bay, Japan Sea is investigated. Both species dwell on coarse bottom sand with the percentage of fine fraction (< 0.2 mm) no more than 3 %. Diatoms are the most important component of the sand dollars feeding, they are represented by 50 species in the ground but only 27 species in the faeces, with predominance of the cells with chloroplasts in the faeces, that indicates a selectivity of the sand dollars feeding. High similarity (0.97) of algal flora in the faeces of S. mirabilis and E. parma shows their common feeding habits. Crystals of zircon and ilmenite with specific gravity 4.7 g/cm3 are accumulated in the diverticulum of S. mirabilis though they are very rare in sandy grounds

Using Noun Phrases for Navigating Biomedical Literature on Pubmed: How Many Updates Are We Losing Track of?

Author-supplied citations are a fraction of the related literature for a paper. The “related citations” on PubMed is typically dozens or hundreds of results long, and does not offer hints why these results are related. Using noun phrases derived from the sentences of the paper, we show it is possible to more transparently navigate to PubMed updates through search terms that can associate a paper with its citations. The algorithm to generate these search terms involved automatically extracting noun phrases from the paper using natural language processing tools, and ranking them by the number of occurrences in the paper compared to the number of occurrences on the web. We define search queries having at least one instance of overlap between the author-supplied citations of the paper and the top 20 search results as citation validated (CV). When the overlapping citations were written by same authors as the paper itself, we define it as CV-S and different authors is defined as CV-D. For a systematic sample of 883 papers on PubMed Central, at least one of the search terms for 86% of the papers is CV-D versus 65% for the top 20 PubMed “related citations.” We hypothesize these quantities computed for the 20 million papers on PubMed to differ within 5% of these percentages. Averaged across all 883 papers, 5 search terms are CV-D, and 10 search terms are CV-S, and 6 unique citations validate these searches. Potentially related literature uncovered by citation-validated searches (either CV-S or CV-D) are on the order of ten per paper – many more if the remaining searches that are not citation-validated are taken into account. The significance and relationship of each search result to the paper can only be vetted and explained by a researcher with knowledge of or interest in that paper