Fantke, Peter

Jolliet, O.

McKone, T.E.

Online Research Database In Technology

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Downloaded from orbit.dtu.dk on: Dec 20, 2017Confronting Health Effects of Particulate Matter in LCIAMcKone, T.E. ; Fantke, Peter; Jolliet, O.Published in:Abstract book - SETAC Europe 24th Annual MeetingPublication date:2014Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):McKone, T. E., Fantke, P., & Jolliet, O. (2014). Confronting Health Effects of Particulate Matter in LCIA. InAbstract book - SETAC Europe 24th Annual Meeting (pp. 92). SETAC Europe.bioavailability in seawater. Taking into account the speciation behaviorof metals in seawater and using effect data exclusively for marineorganisms, two sets of spatially differentiated CFs were developed forthe metals Cd, Co, Cu(II), Ni, Pb and Zn in coastal seawater. One set ofCFs (CFsw-sw) addresses the direct metal emission to coastal seawaterwhile the other set (CFfw-sw) represents the ecotoxicity potential ofmetals in coastal seawater caused by metal emission to freshwaterfollowed by transport to the seawater compartment, taking into accountthe fate of metal in freshwater and in the estuary. CF is the product ofthree factors: Fate Factor (FF), Bioavailability Factor (BF), and EffectFactor (EF). The multimedia fate model of USEtox was used tocalculate the FF. WHAM 7.0 was used to model metal speciationunderlying the BF. Free Ion Activity Model (FIAM) was used to modelEF. The results showed that for a given metal, FFin seawater was higherthan in freshwater, due to longer residence time of the water in thecoastal seawater than in freshwater. The difference between FF inseawater and freshwater was smaller since the difference in waterresidence time was partially neutralized by metal removal in estuaries.Metal BFs in seawater were similar or slightly higher than in freshwaterdue to the lower DOC and SPM concentration in seawater. For mostmetals, EFs were lower in seawater than freshwater, due to lowersensitivity of seawater biota to metals. As a general observation, CFfw-sw was lower than CFsw-sw due to metal removal in freshwater andestuary, but the difference was modest for most metals. For Pb ,seawater CF were up to 1-4 orders of magnitude higher than freshwaterCF. But for the other metals, seawater CFs were similar to freshwaterCFs, indicating that the higher FF and BF in seawater were largelycounterweighed by the lower EF for these metals. The variation of CFsin different coastal seawaters were up to ca. 2-3 orders of magnitude forone metal, indicating the importance of using spatial differentiated CFs.Compared with USES-LCA default CFsw-sw, the new CFsw-sw were atleast 3 orders of magnitude lower for all metals except Pb, of whichUSES-LCA CFsw-sw fall within the range of this study. This impliedthat for some metals, ecotoxicity CFs in coastal seawater might beoverestimated in previous LCIA methods.333 Confronting Health Effects of Particulate Matter in LCIA    T.E. McKone, University of California / Sustainable Energy SystemsGroup; P. Fantke, Technical University of Denmark / IER; O. Jolliet,University of Michigan / School of Public Health.  Ambient particulatematter (PM) is one of the most important environmental stressorscontributing to the global human disease burden. However, there a lackof guidance on how to include health effects from PM exposure in thehealth footprint for life cycle impact assessment (LCIA). A task forcewas initiated to build a PM health impact framework and factors basedon scientific consensus. Existing literature was reviewed and expertinput was collected and discussed in an initial Guidance Workshop. Keyscientific questions and challenges for quantifying health effects fromPM exposure have been discussed and initial guidance andrecommendations for the upcoming impact quantification process weredeveloped. Preliminary recommendations address the generalassessment framework, aspects to determine intake fractions (iF), andaspects to determine exposure-response factors (ERF) along withdisease severity: These recommendations include: (1) the 2011framework proposed by Humbert et al. (doi:10.1021/es103563z)provides an assessment starting point; (2) iF can be used as exposuremetric with breathing rate linking ambient concentration and intake; (3)disability-adjusted life years without age-weighting and discounting canbe used as a health metric; (4) population archetypes can account foraspects influencing intake fractions; (5) spatially-differentiated iF shouldbe established for all archetypes with geographic differentiation furtherdiscussed; (6) emission-weighted iF are needed in all cases whereemission and/or exposure conditions are unclear; (7) the 2010 GlobalBurden of Disease Study provides a useful starting point for calculatinghealth effects; (8) compared to all-cause mortality, cause-specificmortality can provide a more informative basis an LCIA metric but age-and cause-specific disability weights need further analyses; (9) the needremains to discuss whether and how to consistently integrate non-linearexposure-response into LCIA; and (10) PM2.5can be used as indicatorof the health risk associated with PM inhalation exposure. There is notsufficient evidence-based justification to differentiate between differentprimary/secondary PM sources or between different particle sizesregarding toxicity. Our study constitutes a first step towards arriving atrecommendations for how to account for health effects of emissions ofprimary PM and secondary PM precursors in LCIA. However, a rangeof inconclusive aspects requires further analysis.334 Using machine learning for human toxicity and freshwaterecotoxicity characterization of chemical emissions     A. Marvuglia,CRP Henri Tudor / Resource Centre for Environmental TechnologiesCRTEResource Centre for Environmental Technologies CRTE; M.Kanevski, M. Leuenberger, University of Lausanne / Centre derecherche en environnement terrestre CRET; E. Benetto, CRP HenriTudor / Resource Centre for Environmental Technologies CRTE. Toxicity characterization of chemical emissions is a complex task whichusually proceeds via multimedia models attached to models ofdose–response relationships to assess the effects on targets. Differentmodels and approaches are available, but all require a vast amount ofdata on the properties of the chemical compounds being assessed, whichare hard to collect or hardly available (especially for less common ornewly developed chemicals). An example of such models is USEtoxTM,a consensual model for the characterization of human toxicity andfreshwater eco-toxicity. The final aim of this work is building a data-driven model for chemical characterization from a limited amount ofsubstance-specific data, complementary (and not alternative) to theexisting assessment models. By focusing on UsetoxTM, and morespecifically on the modelling of the fate factor (FF) from continentalurban air to urban air (FFairU,airU), for which data are available, thiswork makes a step ahead in that direction by pursuing two mainobjectives: 1) performing for the first time an extensive exploratory dataanalysis (EDA) of the input space containing substance-specificproperties at the aim of detecting particular patterns in the datamanifold; 2) exploring the modelling efficiency (for predicting toxicity)of a set of algorithms based on linear partial least squares (PLS)regression and on non-linear approaches: kernel PLS (KPLS), adaptivegeneral regression neural networks (GRNN) and random forests (RF).The database available in USEtox™ wasused in this study. For the sake ofsimplicity, only the organic compounds have been taken into accounthere, to facilitate the development and testing of the approach pursuedhere, while not affecting the consistency of the outcomes and theirapplicability to the other compounds. Four main explanatory variables(degradation rate in air, degradation rate in water, Henry law coefficientat 25°C and partitioning coefficient between octanol and water) wereidentified in the input space. They are, therefore, the most importantvariables which have to be assessed with a high degree of accuracy.Current research efforts are addressing other parts of the model affectedby important data gaps, e.g. to the calculation of human health effectfactors. The presentation will focus on the results from non-linearalgorithms and on the added value of the data-driven model for the LCAcommunity as a whole and the toxicity assessment practice in particular.335 Feedback from MAgPIE workshop - Environmental riskmitigation measures in risk assessment and management for P     V.Poulsen, ANSES; A. Alix, Dow Agrosciences / Risk Management.  AEuropean workshop under the auspices of SETAC and EuropeanCommision was organised in order to provide European regulatoryauthorities a toolbox of risk mitigation measures designed for the use ofPlant Protection Products for agricultural purposes. During the twoworkshops (one in Rome and one in Madrid) and due to their work in-between, stakeholders provided, for groundwater, surface water, in-fieldterrestrial and soil organisms, and off-field terrestrial organisms: - Asummary of existing risk mitigation measures used in the differentcountries for regulatory purposes, - An analysis of these risk mitigationmeasures according to: their effciciency, their potential use in riskasessment, their practicallity of implementation for farmers, theirpracticallity of implementation from a regulatory point of view, - Acollection of voluntary initiatives and stewardship programmes, -Feedback on experiences in risk management, - Identification of needs92SETAC Europe 24th Annual Meeting

Confronting Health Effects of Particulate Matter in LCIA

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Confronting Health Effects of Particulate Matter in LCIA

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