Spatial differentiation of chemical removal rates from air in life cycle impact assessment

  • Serenella Sala
  • Dimitar Marinov
  • David Pennington



Spatial differentiation is a topic of increasing interest within life cycle assessment (LCA). For chemical-related impacts, in this paper, we evaluate the relative influence of substance properties and of environmental characteristics on the variability in the environmental fate of chemicals using an advanced, spatially resolved model. The goal of this study is to explore spatial distribution and spatial variability of organic chemicals, assessing the variability of the removal rate from air with a multimedia spatially explicit model Multimedia Assessment of Pollutant Pathways in the Environment (MAPPE) Global with a resolution of 1 × 1°. This provides basis to help identify chemicals for which spatial differentiation will be important in LCAs, including whether differentiation will have added benefits over the use of global generic default values, such as those provided by the USEtox model.


A methodology was developed to explore spatial distribution and spatial variability of the fate of organic chemicals. Firstly, guidelines were developed to assign a hypothetical spatial distribution to chemicals which were clustered on the basis of their physical–chemical properties and persistence. Secondly, a test set of 34 representative organic chemicals was used to run MAPPE Global and USEtox model. The results of MAPPE Global were used to highlight spatial variability of removal rate from air amongst different chemicals and their related patterns of variability. A comparison between USEtox and MAPPE Global removal rates from air was performed for each chemical in order to highlight whether spatial differentiation is relevant for the assessment or not.

Results and discussion

Hypothetical spatial distribution of chemical fate was assigned to each combination of physical–chemical properties and persistence. Besides, spatial variability of removal rates from air was assessed running MAPPE model for the test set of 34 chemicals. The variability of results spans from less than one to over four orders of magnitude, showing differences in variability for each cluster of chemicals. Furthermore, different patterns of spatial variability are associated to each cluster of chemical as the spatial pattern is driven by a specific component of the overall removal rate. The comparison between MAPPE and USEtox removal rates from air shows that for 14 out of 34 chemicals within the test set, USEtox values are close to the median of the results of MAPPE. For 11 out of 34, USEtox underestimates the removal rate from air and the results are close to the fifth percentile of MAPPE ones. This is mainly related to how wet/dry deposition and gas exchange are accounted in the two models.

Conclusions and outlook

This work has made further progress towards understanding and implementing how to develop a tailored-made guidance for assessing spatial differentiation in LCA. Results on spatial distribution and spatial variability of chemical are presented as a basis for defining patterns of variability and supporting further development of spatial scenarios and archetypes to be used for life cycle impact assessment. This provides insights into whether using generic global default factors is likely to result in high uncertainty depending on the type of chemical, as well as whether pattern-specific factors would reduce the uncertainty. Uncertainties related to spatial differentiation are presented and discussed.


Spatial differentiation Spatial variability Chemicals fate Life cycle impact assessment of chemicals Removal rates USEtox MAPPE model 



The research was funded by the European Commission under the 7th framework program on environment; ENV.2009. LC-IMPACT - Improved Life Cycle Impact Assessment methods (LCIA) for better sustainability assessment of technologies, Grant agreement number 243827.

Supplementary material

11367_2011_312_MOESM1_ESM.doc (8.2 mb)
ESM 1 (DOC 8.15 MB)


  1. Beyer A, Mackay D, Matthies M, Wania F, Webster E (2000) Assessing long-range transport potential of persistent organic pollutants. Environ Sci Technol 34(4):699–703CrossRefGoogle Scholar
  2. Boethling RS, Fenner K, Howard P, Klečka G, Madsen T, Snape JR, Whelan MJ (2009) Environmental persistence of organic pollutants: guidance for development and review of POP risk profiles. Integr Environ Assess Manag 5:539–556CrossRefGoogle Scholar
  3. Brown TN, Wania F (2009) Development and exploration of an organic contaminant fate model using poly-parameter linear free energy relationships. Environ Sci Technol 43:6676–6683CrossRefGoogle Scholar
  4. Fenner K, Scheringer M, MacLeod M, Matthies M, McKone T, Stroebe M, Beyer A, Bonnell M, Le Gall AC, Klasmeier J, Mackay D, van de Meent D, Pennington D, Scharenberg B, Suzuki N, Wania F (2005) Comparing estimates of persistence and long-range transport potential among multimedia models. Environ Sci Technol 39(7):1932–1942CrossRefGoogle Scholar
  5. Finnveden G, Hauschild MZ, Ekvall T, Guinée J, Heijungs R, Hellweg S, Koehler A, Pennington D, Suh S (2009) Recent developments in life cycle assessment. J Environ Manag 91:1–21CrossRefGoogle Scholar
  6. Gallego A, Rodriguez L, Hospido A, MoreiraMT FG (2010) Development of regional characterisation factors for aquatic eutrophication. Int J Life Cycle Assess 15:32–43CrossRefGoogle Scholar
  7. Geisler G, Hellweg S, Hungerbühler K (2004) Uncertainty analysis in life cycle assessment (LCA). Case study on plant-protection products and implications for decision making. Int J Life Cycle Assess 10:184–192CrossRefGoogle Scholar
  8. Gouin T, Mackay D, Webster E, Wania F (2000) Screening chemicals for persistence in the environment. Environ Sci Technol 34(5):881–884CrossRefGoogle Scholar
  9. Gouin T (2010) The precautionary principle and environmental persistence: prioritizing the decision-making process. Environ Sci Pol 13(3):175–184CrossRefGoogle Scholar
  10. Hauschild M, Potting J (2005) Spatial differentiation in life cycle impact assessment – the EDIP2003 methodology. Environmental News no. 80. The Danish Ministry of the Environment, Environmental Protection Agency, CopenhagenGoogle Scholar
  11. Hertwich EG, McKone TE (2001) Pollutant-specific scale of multimedia models and its implications for the potential dose. Environ Sci Technol 35:142–148CrossRefGoogle Scholar
  12. Hollander A, Pistocchi A, Huijbregts MAJ, Ragas AMJ, Meent DVD (2009) Substance or space ? The relative importance of substance properties and environmental characteristics in modeling the fate of chemicals in Europe. Environ Toxicol Chem 28(1):44–51CrossRefGoogle Scholar
  13. Hong J, Shaked S, Rosenbaum R, Jolliet O (2010) Analytical uncertainty propagation in life cycle inventory and impact assessment: application to an automobile front panel. Int J Life Cycle Assess 15(5):499–510CrossRefGoogle Scholar
  14. Huijbregts MAJ, Gilijamse W, Ragas AMJ, Reijnders L (2003) Evaluating uncertainty in environmental life-cycle assessment a case study comparing two insulation options for a Dutch one-family dwelling. Environ Sci Technol 37(11):2600–2608CrossRefGoogle Scholar
  15. Lahr J, Kooistra L (2010) Environmental risk mapping of pollutants: state of the art and communication aspects. Sci Total Environ 408:3899–3907CrossRefGoogle Scholar
  16. Macdonal RW, Barrie LA, Bidleman TF, Diamond ML, Gregor DJ, Semkin RG, Strachan WM, Li YF, Wania F, Alaee M, Alexeeva LB, Backus SM, Bailey R, Bewers JM, Gobeil C, Halsall CJ, Harner T, Hoff JT, Jantunen LM, Lockhart WL, Mackay D, Muir DC, Pudykiewicz J, Reimer KJ, Smith JN, Stern GA (2000) Contaminants in the Canadian Arctic: 5 years of progress in understanding sources, occurrence and pathways. Sci Total Environ 254:93–234CrossRefGoogle Scholar
  17. Macleod M, Mackay D (2004) Modeling transport and deposition of contaminants to ecosystems of concern: a case study for the Laurentian Great Lakes. Environ Pollut 128(1–2):241–250CrossRefGoogle Scholar
  18. Macleod M, Scheringer M, McKone TE, Hungerbuhler K (2010) The state of multimedia mass-balance modeling in environmental science and decision-making. Environ Sci Technol 44(22):8360–8364CrossRefGoogle Scholar
  19. 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
  20. Margni M, Pennington DW, Bennet DH, Jolliet O (2004) Cyclic exchanges and level of coupling between environmental media: intermedia feedback in multimedia fate models. Environ Sci Technol 38:5450–5457CrossRefGoogle Scholar
  21. Meyer T, Wania F, Breivik K (2005) Illustrating sensitivity and uncertainty in environmental fate models using partitioning maps. Environ Sci Technol 39:3186–3196CrossRefGoogle Scholar
  22. Molander S, Lidholm P, Schowanek D, Recasens M, Fullana P, Christensen FM, Guinee JB, Hauschild M, Jolliet O, Carlson R, Pennington DW, Bachmann TM (2004) OMNIITOX-operational life-cycle impact assessment models and information tools for practitioners. Int J Life Cycle Assess 9(5):282–288CrossRefGoogle Scholar
  23. Muir DCG, Howard PH (2006) Are there other persistent organic pollutants? A challenge for environmental chemists. Environ Sci Technol 40:7157–7166CrossRefGoogle Scholar
  24. OECD (2010) THE OECD Pov and LRTP Screening Tool,3343,en_2649_34373_40754961_1_1_1_1,00.html (accessed December 2010)
  25. 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(4):1119–1128CrossRefGoogle Scholar
  26. Pistocchi A (2008) A GIS-based approach for modeling the fate and transport of pollutants in Europe. Environ Sci Technol 42:3640–3647CrossRefGoogle Scholar
  27. Pistocchi A, Sarigiannis DA, Vizcaino P (2010) Spatially explicit multimedia fate models for pollutants in Europe: state of the art and perspectives. Sci Total Environ 40(18):3817–3830Google Scholar
  28. Pistocchi A, Groenwold J, Lahr J, Loos M, Mujica M, Ragas A, Rallo R, Sala S, Schlink U, Strebel K, Vighi M, Vizcaino P (2011a) Mapping cumulative environmental risks from chemical pollution. Environ Model Assess 16:119–133CrossRefGoogle Scholar
  29. Pistocchi A, Marinov D, Pontes S, Zulian G (2011b) Multimedia assessment of pollutant pathways in the environment - Global scale model (MAPPE Global). EU Report (in preparation)Google Scholar
  30. Rosenbaum RK, Bachmann TM, Gold LS, Huijbregts MAJ, Jolliet O, Juraske R, Köhler 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 characterization factors for human toxicity and freshwater ecotoxicity in life cycle impact assessment. Int J LCA 13(7):532–546CrossRefGoogle Scholar
  31. Scheringer M, Stroebe M, Held H (2002) Chemrange 2.1 – A multimedia transport model for calculating persistence and spatial range of organic chemicals. ETH Zurich,
  32. Scheringer M, Jones KC, Matthies M, Simonich S, Meent DVD (2009) Multimedia partitioning, overall persistence, and long-range transport potential in the context of POPs and PBT chemical assessments. Integr Environ Assess Manag 5(4):557–576CrossRefGoogle Scholar
  33. Seppälä J, Posch M, Johansson M, Hettelingh JP (2006) Country-dependent characterisation factors for acidification and terrestrial eutrophication based on accumulated exceedance as an impact category indicator. Int J Life Cycle Assess 11(6):403–416CrossRefGoogle Scholar
  34. Toose L, Woodfine DG, Macleod M, Mackay D, Gouin J (2004) BETR-World: a geographically explicit model of chemical fate: application to transport of α-HCH to the Arctic. Environ Pollut 128:223–240CrossRefGoogle Scholar
  35. UNEP (2001) Stockholm convention on persistent organic pollutants. United Nations Environment Programme. Geneva, Switzerland. (accessed December 2010)
  36. Van Zelm R, Huijbregts MAJ, Den Hollander HA, Van Jaarsveld HA, Sauter FJ, Struijs J, Van Wijnen HJ, Van de Meent D (2008) European characterization factors for human health damage of PM10 and ozone in life cycle impact assessment. Atmos Environ 42:441–453CrossRefGoogle Scholar
  37. Wania F (2006) Potential of degradable organic chemicals for absolute and relative enrichment in the arctic. Environ Sci Technol 40:569–577CrossRefGoogle Scholar
  38. Wania F, Mackay D (1999) The evolution of mass balance models of persistent pollutant fate in the environment. Environ Pollut 100(1):223–240CrossRefGoogle Scholar
  39. Wegener Sleeswijk A, Heijungs R (2010) GLOBOX: a spatially differentiated global fate, intake and effect model for toxicity assessment in LCA. Sci Total Environ 408:2817–2832CrossRefGoogle Scholar
  40. Wegmann F, Cavin L, Macleod M, Scheringer M, Hungerbuhler K (2009) The OECD software tool for screening chemicals for persistence and long-range transport potential. Environ Model Software 24(2):228–237CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Serenella Sala
    • 1
  • Dimitar Marinov
    • 1
  • David Pennington
    • 1
  1. 1.European Commission—Joint Research Centre-Institute for Environment and Sustainability—Sustainability Assessment UnitIspraItaly

Personalised recommendations