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The establishment of rational frameworks for population-level ecological risk assessment (PLERA) in the context of chemical substances management is an important issue. We illustrate two feasible approaches for establishing predicted-no-effect concentrations (PNECs)for PLERA through a case study of 4-nonylphenol (4-NP) using life-cycle toxicity data for medaka (Oryzias latipes). We first quantified the potential impacts of 4-NP on medaka in terms of reduction of population growth rate (i). An age-classified population matrix model (daily time-step) was developed and used to combine life-cycle survivorship and fecundity data obtained from individual-level responses of medaka expDsed to 4-NP into population-level responses defined by the parameter lambda. Thereafter, from the resulting lambdas, two approaches for establishing population-level PNEC values were proposed and examined. We then derived the PNEC values for population-level impacts, based on (a) the threshold concentration, defined as the chemical concentration at which lambda = 1 as a value with a 95% confidence interval, and (b) the no-observed-effect concentration (NOEC) and the maximum-acceptable-toxic concentration (MATC). The results suggest that PNEC values of 4-NP ranging between 0.82 and 2.10 microg/L affect medaka population growth. Although these approaches have their limitations, current knowledge indicates that they are reasonable and practical for evaluating population-level impacts of chemicals, thereby serving as a case study for establishing PNEC values for PLERA in the context of chemical substances management and decision-making.

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Antibiotics are of concern because of their widespread usage, their potential role in the spread and maintenance of bacterial resistance, and because of the selection pressure on microbes. In this study, the genotoxic potential of 20 quinolone antibacterials, including 5 first-generation, 8 second-generation, and 7 third-generation quinolones, was determined. While all of the antibacterials studied showed genotoxic potential, the molar concentration for each antibacterial that produces 10% (EC10) of the maximum response of corresponding antibacterial ranged from 0.61 to 2917.0 nM, and was greatly dependent on chemical structures. A quantitative structure-activity relationship (QSAR) was established by applying a quantum chemical modeling method to determine the factors required for the genotoxic potential of quinolone antibacterials. The octanol-water coefficient (logP(ow)) adjusted bythe pH and energies of the highest occupied molecular orbital (epsilon(HOMO)) and lowest unoccupied molecular orbital (epsilon(LUMO)) were selected as hydrophobic and electronic chemical descriptors, respectively. The genotoxic potentials of quinolone antibacterials were found to be dependent on their logP(ow) and epsilon(HOMO), while the effects of epsilon(LUMO) on the genotoxic potentials could not be identified. The QSAR model was also used to predict the genotoxic potentials for 14 quinolone antibacterials, including 1 second-generation, 2 third-generation, and 11 fourth-generation quinolone antibacterials. A correlation between the genotoxic potentials and their minimal inhibition concentrations (MIC50) against Streptococcus pneumoniae from the literature for 18 quinolone antibacterials was observed, providing a potential method to estimate MIC50.

Many Jatropha curcas Linnaeus (JCL) plantations have been established in tropical and subtropical regions worldwide. To assess the potential of JCL for biofuel production, the potential areas for JCL plantations, and the yields of JCL must be estimated as accurately as possible. Here, we present a system approach to estimate JCL yields, classify yield levels, and estimate productivity of future JCL plantations. We used a process-based net primary productivity (NPP) model to estimate potential JCL yields. The model estimated that the potential yield of JCL dry seed will vary from 0 to 7.62 ton ha(-1) y(-1), in contrast to estimates of 1.50-7.80 ton ha(-1) y(-1) from previous assessments. We formulated a zoning scheme that takes into account land cover status and potential yield levels. This scheme was used to evaluate the potential area and production of future plantations at the global, regional, and national levels. The estimated potential area of JCL plantations is 59-1486 million hectares worldwide, and the potential production is 56-3613 million ton dry seed y(-1). This study provides scientific information on global patterns of potential plantation areas and yields, which can be used to support bioenergy policy makers to plan commercial-scale JCL plantations.

Ammonia has been proposed as a promising energy carrier and is expected to play a resilient and sustainable role in future energy scenarios. Energy systems critically impact biogeological carbon and nitrogen cycles. Thus, carbon and nitrogen footprints are two important indicators of sustainability for energy systems. In the present study, we explored the optimal supply pathway and identified impact hotspots by investigating the carbon footprint associated with greenhouse gas emissions and the nitrogen footprint associated with reactive nitrogen emissions from the ammonia energy system. Four scenarios (Japan to Japan, JP–JP; Australia to Japan, AU–JP; Chile to Japan, CL–JP; Saudi Arabia to Japan, SA–JP) were modeled based on international relations and energy distribution between these countries. Compared with the Japan electricity mix, it is a win–win situation under scenario JP–JP from the perspective of carbon and nitrogen footprints, while trade-offs were identified under the scenarios AU–JP and CL–JP. SA–JP performed worse in both carbon and nitrogen footprints. Improvement of key processes is critical to mitigate greenhouse gas and reactive nitrogen emissions. When the efficiency of partial oxidation increased by 25% in SA–JP, the carbon and nitrogen footprint decreased by 17% and 8%, respectively. This evaluation relayed information on the sustainable use of ammonia as an energy carrier by examining the relative impacts on both carbon and nitrogen footprints.

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