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Acknowledgements We thank the cohort participants who contributed to these studies and the research staff who collected phenotypic data. Genotyping of the CAGES cohorts and the analyses conducted here were supported by the UK's Biotechnology and Biological Sciences Research Council (BBSRC). Phenotype collection in the Lothian Birth Cohort 1921 was supported by the BBSRC, The Royal Society and The Chief Scientist Office of the Scottish Government. Phenotype collection in the Lothian Birth Cohort 1936 was supported by Research Into Ageing (continues as part of Age UK's The Disconnected Mind project). Phenotype collection in the Aberdeen Birth Cohort 1936 was supported by BBSRC, the Wellcome Trust and Alzheimer's Research UK. Phenotype collection in the Manchester and Newcastle Longitudinal Studies of Cognitive Ageing cohorts was supported by Social Science Research Council, Medical Research Council, Economic and Social Research Council, Research Into Ageing, Wellcome Trust and Unilever plc. The work was undertaken in The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross council Lifelong Health and Wellbeing Initiative (MR/K026992/1). Funding from the BBSRC, EPSRC, ESRC and MRC is gratefully acknowledged. Authors MJ Wright, N K Hansell, SE Medland, NG Martin, and GW Montgomery would like to acknowledge and thank their twin sample for their participation; the Australian Research Council (ARC) for supporting data collection (A7960034, A79906588, A79801419, DP0212016, DP0343921, DP0664638, DP1093900), and the National Health & Medical Research Council (NHMRC) for funding genotyping (Medical Bioinformatics Genomics Proteomics Programme, 389891). SE Medland is supported by an ARC Future Fellowship. Statistical analyses were carried out on the GenEpi Cluster which is financially supported by contributions from grants from the NHMRC (389892;496682;496688;496739;613672) and ARC (FT0991022;FT0991360). ; Peer reviewed ; Publisher PDF

© 2016 American Chemical Society. Engineered nanomaterials (ENMs) are increasingly entering the environment with uncertain consequences including potential ecological effects. Various research communities view differently whether ecotoxicological testing of ENMs should be conducted using environmentally relevant concentrations - where observing outcomes is difficult - versus higher ENM doses, where responses are observable. What exposure conditions are typically used in assessing ENM hazards to populations? What conditions are used to test ecosystem-scale hazards? What is known regarding actual ENMs in the environment, via measurements or modeling simulations? How should exposure conditions, ENM transformation, dose, and body burden be used in interpreting biological and computational findings for assessing risks? These questions were addressed in the context of this critical review. As a result, three main recommendations emerged. First, researchers should improve ecotoxicology of ENMs by choosing test end points, duration, and study conditions - including ENM test concentrations - that align with realistic exposure scenarios. Second, testing should proceed via tiers with iterative feedback that informs experiments at other levels of biological organization. Finally, environmental realism in ENM hazard assessments should involve greater coordination among ENM quantitative analysts, exposure modelers, and ecotoxicologists, across government, industry, and academia.