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A suite of perfluoroalkyl substances (PFASs) and polychlorinated naphthalenes (PCNs) were determined in 41 peregrine falcon eggs collected in South Greenland between 1986 and 2014. Median concentrations of perfluorinated sulfonic acids (ΣPFSA) and perfluorinated carboxylic acids (ΣPFCA) were 303 ng/g dry weight (dw) (58 ng/g wet weight, ww) and 100 ng/g dw (19 ng/g ww), respectively, which was comparable to other studies. Perfluorooctane sulfonate (PFOS) accounted for 94% on average of all PFSAs, but did not show a significant time trend. Perfluorohexane sulfonate (PFHxS), perfluoroheptane sulfonate (PFHpS) and perfluorodecane sulfonate (PFDS) showed non-linear decreases over the study period, while some long-chain PFCAs increased significantly. The PCN profile was dominated by the penta-, hexa- and tetrachlorinated congeners CN-52/60, CN-66/67 and CN-42. CN-54, an indicator of combustion, accounted for 2.4% of ΣPCN on average. All PCN congeners showed a decreasing tendency, which was significant for lipid-normalized concentrations of CN-53, CN-54 and CN-63. The ΣPCN median concentration was 21 ng/g lipid weight, which is in the high end of concentrations reported for bird eggs. The PCN and PFAS concentrations add to an already high contaminant burden and a complex chemical cocktail in the peregrine falcon population in Greenland, mainly reflecting contaminant exposure during migration and winter stays in Central and South America., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Median levels of regulated flame retardants, i.e., polybrominated diphenyl ethers (PBDEs), brominated biphenyl (BB)-153, and hexabromocyclododecane (HBCD), in 33-48 eggs of peregrine falcons (Falco peregrinus) from Greenland were 1909, 359, and 5.98 ng/g lipid weight (lw), respectively, and generally intermediate to levels in North America and Europe. Unregulated flame retardants had lower median concentrations of 1.06 (2-ethylhexyl-2,3,4,5-tetrabromobenzoate, EH-TBB), 2.42 (1,2-bis(2,4,6-tribromophenoxy)-ethane, BTBPE), 0.52 (2,4,6-tribromophenyl 2,3-dibromopropyl ether, DPTE), and 4.78 (dechlorane plus) ng/g lw. Although these compounds are often described as recent replacements for PBDEs, they were also present in eggs from the 1980s. BDE-209 was the only compound with a significant increase (+7.2% annual change) between 1986 and 2014, while BB-153 and DPTE decreased significantly (-8.0% and -2.8% annual change, respectively). Dechlorane plus showed a nonsignificant increase. Individual birds, equipped with light-logging geolocators, confirmed the contaminant exposure over a large geographical area as the birds spent nearly equal time periods in their breeding and wintering grounds in Greenland and Central/South America, respectively, interrupted by 5-6 weeks of migration through North America.

Conducting environmental risk assessment of engineered nanomaterials has been an extremely challenging endeavor thus far. Moreover, recent findings from the nano-risk scientific community indicate that it is unlikely that many of these challenges will be easily resolved in the near future, especially given the vast variety and complexity of nanomaterials and their applications. As an approach to help optimize environmental risk assessments of nanomaterials, we apply the Worst-Case Definition (WCD) model to identify best estimates for worst-case conditions of environmental risks of two case studies which use engineered nanoparticles, namely nZVI in soil and groundwater remediation and C(60) in an engine oil lubricant. Results generated from this analysis may ultimately help prioritize research areas for environmental risk assessments of nZVI and C(60) in these applications as well as demonstrate the use of worst-case conditions to optimize future research efforts for other nanomaterials. Through the application of the WCD model, we find that the most probable worst-case conditions for both case studies include i) active uptake mechanisms, ii) accumulation in organisms, iii) ecotoxicological response mechanisms such as reactive oxygen species (ROS) production and cell membrane damage or disruption, iv) surface properties of nZVI and C(60), and v) acute exposure tolerance of organisms. Additional estimates of worst-case conditions for C(60) also include the physical location of C(60) in the environment from surface run-off, cellular exposure routes for heterotrophic organisms, and the presence of light to amplify adverse effects. Based on results of this analysis, we recommend the prioritization of research for the selected applications within the following areas: organism active uptake ability of nZVI and C(60) and ecotoxicological response end-points and response mechanisms including ROS production and cell membrane damage, full nanomaterial characterization taking into account detailed information on nanomaterial surface properties, and investigations of dose-response relationships for a variety of organisms., (Copyright © 2011 Elsevier B.V. All rights reserved.)

This paper helps bridge the gap between scientists and other stakeholders in the areas of human and environmental risk management of chemicals and engineered nanomaterials. This connection is needed due to the evolution of stakeholder awareness and scientific progress related to human and environmental health which involves complex methodological demands on risk management. At the same time, the available scientific knowledge is also becoming more scattered across multiple scientific disciplines. Hence, the understanding of potentially risky situations is increasingly multifaceted, which again challenges risk assessors in terms of giving the 'right' relative priority to the multitude of contributing risk factors. A critical issue is therefore to develop procedures that can identify and evaluate worst case risk conditions which may be input to risk level predictions. Therefore, this paper suggests a conceptual modelling procedure that is able to define appropriate worst case conditions in complex risk management. The result of the analysis is an assembly of system models, denoted the Worst Case Definition (WCD) model, to set up and evaluate the conditions of multi-dimensional risk identification and risk quantification. The model can help optimize risk assessment planning by initial screening level analyses and guiding quantitative assessment in relation to knowledge needs for better decision support concerning environmental and human health protection or risk reduction. The WCD model facilitates the evaluation of fundamental uncertainty using knowledge mapping principles and techniques in a way that can improve a complete uncertainty analysis. Ultimately, the WCD is applicable for describing risk contributing factors in relation to many different types of risk management problems since it transparently and effectively handles assumptions and definitions and allows the integration of different forms of knowledge, thereby supporting the inclusion of multifaceted risk components in cumulative risk management., (Copyright 2009 Elsevier B.V. All rights reserved.)

This paper illustrates, by a case study, how to apply the conceptual Worst-Case Definition (WCD) model, developed in the methodological paper in the current journal, by Sørensen et al. (2010-this issue). The case is about eco-toxicological risk assessment of pesticides under Danish conditions. Cumulative aspects are included on a conceptual basis as elements of the worst-case conditions. This defines factors that govern the risk assessment, including location in time and space of risk "hotspots". Two pillars of concern drive the conceptual modelling: (1) What to protect (denoted Protected Units (PUs)) and (2) the reason for increased risk level (denoted Causes of Risks (CRs)). Both PUs and CRs are analysed using hierarchical procedures that facilitate a complete listing of concrete factors governing increased risk for adverse effect due to agricultural usage of pesticide. The factors governing pesticide risk are combined in a context that combines the protection of relevant groupings of organisms with the factors for increased risk level for each of these. Identification of the most important relations between defined types of PUs and CRs is illustrated using expert knowledge. Existing databases are used to form spatial distributed risk indicators as estimators for a selection of important relations between PUs and CRs. This paper illustrates how the WCD model can break down the complex issue of uncertainty into fractions that are more open for evaluations. Finally, it shows application of risk indicators in a multi-criterion analysis using respectively self organizing mapping and partial order technique in a comparative analysis that highlights critical aspects of uncertainty, due to the ambiguity between single risk indicator rankings., (Copyright 2009 Elsevier B.V. All rights reserved.)

In 1997 a Pollutant Release and Transfer Register (PRTR) pilot project was initiated in Japan. In 1998 the project was expanded and in 1999 a law concerning the establishment of a national PRTR was adopted. Data on the emissions of chemical substances are therefore now being reported on a continuous base. In relation to the PRTR project data on toxicity have been collected. In order to make efficient use of the collected information on emission and toxicity it is useful to group or rank the chemical substances according to the impact on human health and the environment. It has recently been argued that partial order theory (POT) in combination with the use of linear extensions (LE) may be the most objective way to create a linear rank. The methodology has been further expanded to handle larger data sets by the use of random linear extensions (RLE). In this paper the Japanese PRTR data are ranked using the POT/RLE methodology. An average rank is established for chemical substances in the 1998 and 1999 PRTR in Japan. The top 10 chemical substances in the 1998 PRTR are: dichlorvos, inorganic arsenic compounds, cobalt compounds, beryllium compounds, fenitrothion, disulfoton, parathion, diazinon, 4,4'-diamino-3,3'-dichlorodiphenylmethane and antimony compounds. The top 10 chemical substances from the 1999 PRTR are PCBs, lead compounds, fenitrothion, dichlorvos, disulfoton, inorganic arsenic compounds, chlorothalonil, thiobencarb, chromium and HCFC-141b. The descriptor having the highest influence on the ranking of the 1998 PRTR data is the production volume, which, however, is not given in the 1999 PRTR. Further, the disagreement between the ranking with the lack of toxicity data substituted with mean and maximum values, respectively, strongly indicates a general need for further toxicological investigations.