Annie Weisbrod

Explaining differences between bioaccumulation measurements in laboratory and field data through use of a probabilistic modeling approach.

In the regulatory context, bioaccumulation assessment is often hampered by substantial data uncertainty as well as by the poorly understood differences often observed between results from laboratory and field bioaccumulation studies. Bioaccumulation is a complex, multifaceted process, which calls for accurate error analysis. Yet, attempts to quantify and compare propagation of error in bioaccumulation metrics across species and chemicals are rare. Here, we quantitatively assessed the combined influence of physicochemical, physiological, ecological, and environmental parameters known to affect bioaccumulation for 4 species and 2 chemicals, to assess whether uncertainty in these factors can explain the observed differences among laboratory and field studies. The organisms evaluated in simulations including mayfly larvae, deposit-feeding polychaetes, yellow perch, and little owl represented a range of ecological conditions and biotransformation capacity. The chemicals, pyrene and the polychlorinated biphenyl congener PCB-153, represented medium and highly hydrophobic chemicals with different susceptibilities to biotransformation. An existing state of the art probabilistic bioaccumulation model was improved by accounting for bioavailability and absorption efficiency limitations, due to the presence of black carbon in sediment, and was used for probabilistic modeling of variability and propagation of error. Results showed that at lower trophic levels (mayfly and polychaete), variability in bioaccumulation was mainly driven by sediment exposure, sediment composition and chemical partitioning to sediment components, which was in turn dominated by the influence of black carbon. At higher trophic levels (yellow perch and the little owl), food web structure (i.e., diet composition and abundance) and chemical concentration in the diet became more important particularly for the most persistent compound, PCB-153. These results suggest that variation in bioaccumulation assessment is reduced most by improved identification of food sources as well as by accounting for the chemical bioavailability in food components. Improvements in the accuracy of aqueous exposure appear to be less relevant when applied to moderate to highly hydrophobic compounds, because this route contributes only marginally to total uptake. The determination of chemical bioavailability and the increase in understanding and qualifying the role of sediment components (black carbon, labile organic matter, and the like) on chemical absorption efficiencies has been identified as a key next steps.

Global guidance on environmental life cycle impact assessment indicators: findings of the scoping phase

Olivier Jolliet & Rolf Frischknecht & Jane Bare & Anne-Marie Boulay & Cecile Bulle & Peter Fantke & Shabbir Gheewala & Michael Hauschild & Norihiro Itsubo & Manuele Margni & Thomas E. McKone & Llorenc Mila y Canals & Leo Postuma & Valentina Prado-Lopez & Brad Ridoutt & Guido Sonnemann & Ralph K. Rosenbaum & Tom Seager & Jaap Struijs & Rosalie van Zelm & Bruce Vigon & Annie Weisbrod & with contributions of the other workshop participants

Indicator selection in life cycle assessment to enable decision making: issues and solutions

PurposeWith an ever increasing list of indicators available, life cycle assessment (LCA) practitioners face the challenge of effectively communicating results to decision makers. Simplification of LCA is often limited to an arbitrary selection of indicators, use of single scores by using weighted values or single attribute indicators. These solutions are less attractive to decision makers, since value judgments are introduced or multi-indicator information is lost. Normalization could be a means to narrow the list of indicators by ranking indicators vs. a reference system. This paper shows three different normalization approaches that produce very different ranking of indicators. It is explained how normalization helps maintain a multi-indicator approach while keeping the most relevant indicators, allowing effective decision making.MethodsThe approaches are illustrated on a hand dishwashing case study, using ReCiPe as the impact assessment method and taking the European population (year 2000) as the reference situation. Indicators are ranked using midpoint normalization factors, and compared to the ranking from endpoint normalization broken down by midpoint contribution.Results and discussionEndpoint normalization shows Resources as the most relevant area of protection for this case, closely followed by Human Health and Ecosystem. Broken down by their key driving midpoints, fossil depletion, climate change and, to a lesser extent, particulate matter formation and metal depletion, are most relevant. Midpoint normalization, however, indicates Freshwater Eutrophication, Natural Land Transformation and Toxicity indicators (marine and freshwater ecotoxicity and human toxicity) are most relevant.ConclusionsA three-step approach based on endpoint normalization is recommended to present only the most relevant indicators, allowing more effective decision making instead of communicating all LCA indicators. The selection process breaks out the normalized endpoint results into the most contributing midpoints (relevant indicators) and reports results with midpoint level units. Bias due to lack of data completeness is less of an issue in the endpoint normalization process (compared to midpoint normalization), while midpoint results are less subject to uncertainty (compared to endpoint results). Focusing on the relevant indicators and key contributing unit processes has proven to be effective for non-LCA expert decision makers to understand, use, and communicate complex LCA results.

A Review of the Airborne and Waterborne Emissions from Uncontrolled Solid Waste Disposal Sites

ABSTRACT The objective of this review is to critically analyze literature, data, and models on the environmental releases from the uncontrolled disposal and burning of solid waste. Major concerns include releases of greenhouse gases, particulate matter, and leachate. Many factors influence these releases including waste composition, site depth, and climate. While the impact of these factors is understood qualitatively, there is little data and considerable uncertainty in model predictions. One limitation is that in general, predicted emissions are not responsive to changes in waste composition. Estimating impacts to human health and the environment from the predicted emissions results in additional uncertainty.

Erratum to: Global guidance on environmental life cycle impact assessment indicators: Findings of the scoping phase (International Journal of Life Cycle Assessment DOI: 10.1007/s11367-014-0703-8)

Olivier Jolliet & Rolf Frischknecht & Jane Bare & Anne-Marie Boulay & Cecile Bulle & Peter Fantke & Shabbir Gheewala & Michael Hauschild & Norihiro Itsubo & Manuele Margni & Thomas E. McKone & Llorenç Mila y Canals & Leo Posthuma & Valentina Prado-Lopez & Brad Ridoutt & Guido Sonnemann & Ralph K. Rosenbaum & Tom Seager & Jaap Struijs & Rosalie van Zelm & Bruce Vigon & Annie Weisbrod & with contributions of the other workshop participants