Spatial differentiation of chemical removal rates from air in life cycle impact assessment
- 349 Downloads
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.
KeywordsSpatial 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.2009.3.3.2.1: LC-IMPACT - Improved Life Cycle Impact Assessment methods (LCIA) for better sustainability assessment of technologies, Grant agreement number 243827.
- 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
- 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
- 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
- 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
- OECD (2010) THE OECD Pov and LRTP Screening Tool http://www.oecd.org/document/17/0,3343,en_2649_34373_40754961_1_1_1_1,00.html (accessed December 2010)
- 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
- 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
- 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
- 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, www.sust-chem.ethz.ch/research/groups/prod_assessment/Projects/chemrange/index
- UNEP (2001) Stockholm convention on persistent organic pollutants. United Nations Environment Programme. Geneva, Switzerland. http://chm.pops.int (accessed December 2010)