Global spatial analysis of toxic emissions to freshwater: operationalization for LCA

  • Anna Kounina
  • Manuele Margni
  • Andrew D. Henderson
  • Olivier Jolliet



There is increasing interest in using fate and exposure models to spatially differentiate the impacts of chemical emissions. This work aims at exploring the operationalization in life cycle assessment (LCA) of spatially differentiated models for toxic emissions into freshwater. We analyse and compare the variability of fate and exposure factors at high resolution with aggregated factors at different levels of lower resolution.


We developed a spatially resolved fate and exposure characterization model and factors for toxic emissions into freshwater with global coverage at 0.5° × 0.5° resolution, extending a global hydrological model to account for removal processes, namely chemical and biological degradation, sedimentation, and volatilization. We analysed the variation in fate and exposure factors for water ingestion, identifying the main factors of influence. We then developed archetypes for ecosystems and human fate and exposure. Using a case study of emissions of arsenic from red mud disposal as a waste from alumina production, we tested practical solutions to apply spatial characterization factors aggregated at different resolution in LCA, comparing archetype-based with region-based approaches.

Results and discussion

World maps show up to 5 orders of magnitude variation for chemical fate in fresh water across all 0.5° × 0.5° grid cells and up to 15 orders of magnitude for human intake fractions. The freshwater residence time to the sea and the equivalent depth—over all downstream cells—were the most influential landscape parameters. They were used to define four freshwater landscape archetypes. These archetypes capture variation in fate well, better than country or continent-aggregated values, but are not able to reflect variation in intake fraction. The case study on arsenic from alumina production shows that the determination of industry-specific weighted average represents a pragmatic way to account for sector-specific location of emissions. The population-weighted approach is primarily applicable for emissions that are related to population density, such as household emissions.


The developed global freshwater model demonstrates large spatial variations in fate and exposure. Archetypes for fate in fresh water provide substantial reductions in variability compared to country or continental averages, but are more difficult to apply to LCA than rural or urban archetypes for air emissions. The 0.5° × 0.5° grid model and the fate archetypes may also be used in the context of ecological scenarios to identify hotspots. In practice, population-weighted and sector-specific average characterization factors may represent the most operational way to account for specific distribution patterns of toxic emissions in LCA.


Chemical fate Ecotoxicity Freshwater Global modeling Human toxicity Intake fractions Life cycle assessment Spatial differentiation 



The authors would like to thank Cedric Wannaz for his support for the use of the ArcGIS software and Yan Dong for discussions on metal speciation, as well as Francis Gasser and Dr. Yoshihide Wada for providing spatialized hydrological data.


This work is financially supported by the project Life Cycle Impact Assessment Methods for Improved Sustainability Characterisation of Technologies (LC-IMPACT), contract no. 243827, funded by the European Commission under the Seventh Framework Programme, as well as by the International Aluminium Institute through a grant given to University of Michigan.

Supplementary material

11367_2018_1476_MOESM1_ESM.docx (2 mb)
ESM 1 (DOCX 2040 kb)


  1. Alexander RB, Smith RA, Schwarz GE (2004) Estimates of diffuse phosphorus sources in surface waters of the United States using a spatially referenced watershed model. Water Sci Technol 49:1–10CrossRefGoogle Scholar
  2. Allison JD, Allison TL (2005) Partition coefficients for metals in surface water, soil, and waste. US EPA, WashingtonGoogle Scholar
  3. ATSDR (2007) Toxicological profile for arsenic. US Department of Health and Human Services. Agency for Toxic Substances and Disease Registry, Atlanta Google Scholar
  4. Bennett D, McKone T, Evans J, Nazaroff W, Margni M, Jolliet O And Smith KR (2002) Defining intake fraction. Environ Sci Technol 36 (9):207A–211ACrossRefGoogle Scholar
  5. Borak J, Hosgood HD (2007) Seafood arsenic: implications for human risk assessment. Regul Toxicol Pharmacol 47(2):204–212CrossRefGoogle Scholar
  6. Bourgault G (2014) Gestion de l’incertitude causée par l’incohérence d’échelle spatiale à l’interface de l’inventaire et de l’analyse des impacts en ACV. PhD thesis at Ecole Polytechnique de Montréal, pp 1–243Google Scholar
  7. CIESIN (2005) Gridded Population of the World (GPW), v3. Population density grid. University. Columbio University. Accessed at: Columbia, USA
  8. Den Hollander H, Van Eijkeren J, Van de Meent D (2004) SimpleBox 3.0: Multimedia mass balance model for evaluating the fate of chemicals in the environment. National Institute for Public Health and the Environment, Bilthoven. Report no. 601200003. Bilthoven, The NetherlandsGoogle Scholar
  9. Dzombak DA, Morel FMM (1990) Surface complexation modeling: hydrous ferric oxide. Wiley, New York, pp 1–416Google Scholar
  10. Edmonds JS, Francesconi KA (1993) Arsenic in seafoods: human health aspects and regulations. Mar Pollut Bull 26(12):665–674CrossRefGoogle Scholar
  11. FAO (2014) AQUASTAT database, Food and Agriculture Organization of the United Nations (FAO). Accessed at:
  12. Franco A, Price O, Marshall S, Jolliet O, Van den Brink P, Rico A, Focks A, De Laender F, Ashauer R (2016) Towards refined environmental scenarios for ecological risk assessment of down-the-drain chemicals in freshwater environments. Integr Environ Assess Manag 13(2):233–248CrossRefGoogle Scholar
  13. Gandhi N, Diamond ML, van de Meent D, Huijbregts MAJ, Peijnenburg WJGM, Guinée J (2010) New method for calculating comparative toxicity potential of cationic metals in freshwater: application to copper, nickel, and zinc. Environ Sci Technol 44:5195–5201CrossRefGoogle Scholar
  14. Hellweg S, Demou E, Bruzzi R, Meijer A, Rosenbaum RK, Huijbregts MAJ, McKone TE (2009) Integrating human indoor air pollutant exposure with life cycle impact assessment. Environ Sci Technol 43:1670–1679CrossRefGoogle Scholar
  15. Helmes RJK, Huijbregts M a. J, Henderson AD, Jolliet O (2012) Spatially explicit fate factors of phosphorous emissions to freshwater at the global scale. Int J Life Cycle Assess 17:646–654Google Scholar
  16. Henderson AD, Hauschild MZ, Meent D et al (2011) USEtox fate and ecotoxicity factors for comparative assessment of toxic emissions in life cycle analysis: sensitivity to key chemical properties. Int J Life Cycle Assess 16:701–709CrossRefGoogle Scholar
  17. Huijbregts M, Margni M, Jolliet O et al (2010) USEtoxTM chemical database: inorganics. USEtoxTM team publication, pp 1–11Google Scholar
  18. Humbert S, Marshall JD, Shaked S, Spadaro JV, Nishioka Y, Preiss P, McKone TE, Horvath A, Jolliet O (2011) Intake fraction for particulate matter: recommendations for life cycle impact assessment. Environ Sci Technol 45:4808–4816CrossRefGoogle Scholar
  19. Kim E, Little JC, Chiu N, Chiu A (2001) Inhalation exposure to volatile chemicals in drinking water. J Environ Sci Health 19(2):387-413CrossRefGoogle Scholar
  20. Koormann F, Rominger J, Schowanek D, Wagner JO, Schröder R, Wind T, Silvani M, Whelan MJ (2006) Modeling the fate of down-the-drain chemicals in rivers: an improved software for GREAT-ER. Environ Model Softw 21:925–936CrossRefGoogle Scholar
  21. Kounina A, Margni M, Shaked S, Bulle C, Jolliet O (2014) Spatial analysis of toxic emissions in LCA: a sub-continental nested USEtox model with freshwater archetypes. Environ Int 69:67–89CrossRefGoogle Scholar
  22. Lehner B, Verdin K, Jarvis A (2006) HydroSHEDS technical documentation. Accessed at:
  23. Macleod M, Bennett DH, Perem M et al (2004) Dependence of intake fraction on release location in a multimedia framework a case study of four contaminants in North America. J Ind Ecol 8:89–102CrossRefGoogle Scholar
  24. Manneh R, Margni M, Deschênes L (2010) Spatial variability of intake fractions for Canadian emission scenarios: a comparison between three resolution scales. Environ Sci Technol 44:4217–4224CrossRefGoogle Scholar
  25. Mutel CL, Hellweg S (2009) Regionalized life cycle assessment: computational methodology and application to inventory databases. Environ Sci Technol 43:5797–5803CrossRefGoogle Scholar
  26. National Environmental Research Institute (2010) Heavy metal emissions for Danish road transport. NERI Technical Report No. 780, pp 1–103Google Scholar
  27. Owsianiak M, Rosenbaum RK, Huijbregts MA, Hauschild MZ (2013) Addressing geographic variability in the comparative toxicity potential of copper and nickel in soils. Environ Sci Technol 47:3241–3250CrossRefGoogle Scholar
  28. Parekh NA (2012) Assessment of phosphorus fractions in streams draining different land use and development of new monitoring method. Master thesis at the University of Oslo, pp 1–155Google Scholar
  29. Pennington DW, Margni M, Ammann C, Jolliet O (2005) Multimedia fate and human intake modeling: spatial versus nonspatial insights for chemical emissions in Western Europe. Environ Sci Technol 39:1119–1128CrossRefGoogle Scholar
  30. Pennington DW, Margni M, Payet J, Jolliet O (2006) Risk and regulatory hazard-based toxicological effect indicators in life-cycle assessment (LCA). Hum Ecol Risk Assess 12:450–475CrossRefGoogle Scholar
  31. Pistocchi A, Zulian G, Vizcaino P, Marinov D (2010) Multimedia assessment of pollutant pathways in the environment, European scale model. JRC scientific and technical reports, pp 1–55Google Scholar
  32. Quantis (2012) Quantis Water Database v1.3. Accessed at:
  33. Rosenbaum RK, Bachmann TM, Gold LS, Huijbregts MAJ, Jolliet O, Juraske R, Koehler A, Larsen HF, MacLeod M, Margni M, McKone TE, Payet J, Schuhmacher M, van de Meent D, Hauschild MZ (2008) USEtox—the UNEP-SETAC toxicity model: recommended characterisation factors for human toxicity and freshwater ecotoxicity in life cycle impact assessment. Int J Life Cycle Assess 13:532–546CrossRefGoogle Scholar
  34. Schwarzenbach RP, Gschwend PM, Imboden DM (2003) Environmental organic chemistry. John Wiley, pp 1327Google Scholar
  35. Sedlbauer VK, Braune A, Humbert S et al (2007) Spatial differentiation in LCA: moving forward to more operational sustainability. Technikfolgenabschätzung Theorie Prax 3:24–31Google Scholar
  36. Stumm W, Morgan JJ (1995) Aquatic chemistry: chemical equilibria and rates in natural waters. Wiley, New York, p 1040Google Scholar
  37. Tabereaux AT, Peterson RD (2014) Aluminum production. In: Treatise on process metallurgy: industrial processes, volume 3: industrial processes, pp 839–917Google Scholar
  38. U.S. Census Bureau (2014) In: Census 2000 Urban and Rural Classification.
  39. U.S. Environmental Protection Agency (2012) EPI SuiteTM v4.11. Accessed at:
  40. Vörösmarty C, Fekete B, Meybeck M, Lammers R (2000) Geomorphometric attributes of the global system of rivers at 30-minute spatial resolution. J Hydrol 237:17–39CrossRefGoogle Scholar
  41. Wannaz C, Franco A, Kilgallon J, Hodges J, Jolliet O (2017) A global framework to model spatial ecosystems exposure to home and personal care chemicals in Asia. Sci Total Environ 622-623:410–420CrossRefGoogle Scholar
  42. Water systems analysis group (2014) World water development report II. Indicators for world water assessment programme. Accessed at:

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Quantis, Innovation Park, EPFLLausanneSwitzerland
  2. 2.Research Group on the Economics and Management of the EnvironmentEDCE, Ecole Polytechnique Fédérale de LausanneLausanneSwitzerland
  3. 3.CIRAIG, École Polytechnique of MontréalMontréalCanada
  4. 4.School of Public HealthUniversity of Texas Health Science Center at HoustonHoustonUSA
  5. 5.Environmental Health Sciences, School of Public HealthUniversity of MichiganAnn ArborUSA

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