Arsenic compounds accumulated in pearl oyster Pinctada fucata

We investigated the water-soluble arsenic compounds present in the soft tissues of both the pearl-free and the pearl-containing pearl oysters. After dividing the soft tissue into five parts, i.e., adductor muscle, foot, mantle, viscera and gill, each part was analyzed by high-performance liquid chromatography-inductively coupled plasma mass spectrometry for the arsenic compounds accumulated in it. Arsenic concentration of each tissue part ranged from 22.1 to 45.7 microg g(-1) of dry tissue in the pearl-free pearl oyster and from 27.4 to 50.4 microg g(-1) of dry tissue in the pearl-containing pearl oyster. On the grounds of the present evidence the major water-soluble arsenic compound accumulated in each part was identified as arsenobetaine without exception in both types of pearl oysters (94.3-99.7% in the pearl-free pearl oyster and 87.2-99.7% in the pearl-containing pearl oyster). Trace or small amounts of arsenic compounds including tetramethylarsonium ion and arsenocholine were also detected in some parts. The levels of these minor arsenicals were a little higher in pearl-free pearl oyster than in the pearl-containing pearl oyster. This study confirms the hygienic safety of the soft tissues of both the pearl-free and the pearl-containing pearl oysters, as food.     

Occurrence of organo-arsenicals in jellyfishes and their mucus

Water-soluble arsenic compound fractions were extracted from seven species of jellyfishes and subjected to analysis by high-performance liquid chromatography-inductively coupled plasma mass spectrometry (HPLC-ICP-MS) for arsenicals. A low content of arsenic was found to be the characteristic of jellyfish. Arsenobetaine (AB) was the major arsenic compound without exception in the tissues of the jellyfish species and mucus-blobs collected from some of them. Although the arsenic content in Beroe cucumis, which preys on Bolinopsis mikado, was more than 13 times that in B. mikado, the chromatograms of these two species were similar in the distribution pattern of arsenicals. The nine species of jellyfishes including two species treated in the previous paper can be classified into arsenocholine (AC)-rich and AC-poor species. Jellyfishes belonging to Semaostamae were classified as AC-rich species.     

Microbial degradation of arsenobetaine, the major water soluble organoarsenic compound occurring in marine animals

The conversion of arsenobetaine into other arsenic compounds by microorganisms occurring in the bottom sediments from coastal waters was demonstrated for the first time. Three arsenic metabolites were observed on high performance liquid chromatogram during incubation at 25 °C for 80 days in 2 kinds of media, 1/5 ZoBell 2216E and a medium composed of inorganic salts. The time course pattern of arsenic metabolites during incubation was fairly different between these 2 media.     

Technological feasibility and costs of achieving a 50 % reduction of global GHG emissions by 2050: mid- and long-term perspectives

In this article we examine the technological feasibility of the global target of reducing GHG emissions to 50 % of the 1990 level by the year 2050. We also perform a detailed analysis of the contribution of low-carbon technologies to GHG emission reduction over mid- and long-term timeframes, and evaluate the required technological cost. For the analysis we use AIM/Enduse[Global], a techno-economic model for climate change mitigation policy assessment. The results show that a 50 % GHG emission reduction target is technically achievable. Yet achieving the target will require substantial emission mitigation efforts. The GHG emission reduction rate from the reference scenario stands at 23 % in 2020 and 73 % in 2050. The marginal abatement cost to achieve these emission reductions reaches 150/tCO < sub > 2 < /sub > -eq in 2020 and150/tCO₂-eq in 2020 and 600/tCO₂-eq in 2050. Renewable energy, fuel switching, and efficiency improvement in power generation account for 45 % of the total GHG emission reduction in 2020. Non-energy sectors, namely, fugitive emission, waste management, agriculture, and F-gases, account for 25 % of the total GHG emission reduction in 2020. CCS, solar power generation, wind power generation, biomass power generation, and biofuel together account for 64 % of the total GHG emission reduction in 2050. Additional investment in GHG abatement technologies for achieving the target reaches US6.0 trillion by 2020 and US 6.0 trillion by 2020 and US 73 trillion by 2050. This corresponds to 0.7 and 1.8 % of the world GDP, respectively, in the same periods. Non-Annex I regions account for 55 % of the total additional investment by 2050. In a sectoral breakdown, the power generation and transport sectors account for 56 and 30 % of the total additional investment by 2050, respectively.     

Low-carbon transitions in world regions: comparison of technological mitigation potential and costs in 2020 and 2030 through bottom-up analyses

This study focuses on low-carbon transitions in the mid-term and analyzes mitigation potentials of greenhouse gas (GHG) emissions in 2020 and 2030 in a comparison based on bottom-up-type models. The study provides in-depth analyses of technological mitigation potentials and costs by sector and analyzes marginal abatement cost (MAC) curves from 0 to 200 US $/tCO₂ eq in major countries. An advantage of this study is that the technological feasibility of reducing GHG emissions is identified explicitly through looking at distinct technological options. However, the results of MAC curves using the bottom-up approach vary widely according to region and model due to the various differing assumptions. Thus, this study focuses on some comparable variables in order to analyze the differences between MAC curves. For example, reduction ratios relative to 2005 in Annex I range from 9 % to 31 % and 17 % to 34 % at 50 US $/tCO₂ eq in major countries. An advantage of this study is that the technological feasibility of reducing GHG emissions is identified explicitly through looking at distinct technological options. However, the results of MAC curves using the bottom-up approach vary widely according to region and model due to the various differing assumptions. Thus, this study focuses on some comparable variables in order to analyze the differences between MAC curves. For example, reduction ratios relative to 2005 in Annex I range from 9 % to 31 % and 17 % to 34 % at 50 US /tCO₂ eq in 2020 and 2030, respectively. In China and India, results of GHG emissions relative to 2005 vary very widely due to the difference in baseline emissions as well as the diffusion rate of mitigation technologies. Future portfolios of advanced technologies and energy resources, especially nuclear and renewable energies, are the most prominent reasons for the difference in MAC curves. Transitions toward a low-carbon society are not in line with current trends, and will require drastic GHG reductions, hence it is important to discuss how to overcome various existing barriers such as energy security constraints and technological restrictions.