Understanding the Life Cycle Impact Assessment Process: Part III – Modeling Effects and Conclusion
This is the final installment in a three-part series that reviews the three distinct phases of LCIA impact modeling (fate, exposure, and effect (Exhibit 1)), and explains how they build on one another to create a usable picture of environmental impacts for use in broader analyses.
As discussed earlier, the LCIA process mathematically models the possible outcomes of an upstream decision to mimic the natural processes the emission will go through, and the impact it will have on human health and plant and animal species.
In Part I, we reviewed the use of fate modeling to account for the characteristics of an emission and the environmental concentration it forms once released. That information let us move on to exposure modeling, discussed in Part II , which looks at the intake level of the emission by considering various routes and modes of intake. Now, having assessed exposure, we can model the effect on organisms by linking the exposure information to known toxicity data at given intake levels.
Our models link exposure factors to adverse effects at a given exposure level by an effect factor. Effect is often determined with toxicity data, using dose-response curves that represent the toxicological effects of a pollutant on a population at different doses. For human toxicity, these data could be based on ED50 (the dose at which 50% of the individuals of a population are affected), while ecosystem analyses often use HC50 (the hazardous concentration at which 50% of species are affected). Note that the ED50 data are generally based on tests in rats, mice, hamsters and nonhuman primates. The effect factor for human toxicity is measured as cases per kg intake, while the effect factor for ecosystems is measured as the fraction of species exposed to concentration above their EC50 (Potentially Affected Fraction of Species, PAF per kg/m3).
The following expressions (Equation 1) can be used for calculating effect on humans and ecosystems:
Tying Emission to Impact: Characterization Factors
For the toxicity impact category, characterization includes fate, exposure and effect, although other impact categories might not use all three factors (Exhibit 2).
Smog and Particulate Matter impacts, for example, may use only fate and exposure, while Climate Change and Acidification impacts may use only fate. Please note that the characterization factors can also be defined at the damage level which is the endpoint of the cause-effect chain. The calculation of the damage factors generally involves the use of fate, exposure and effect.
We hope you’ve found this discussion of LCIA useful; if you have questions or comments, please feel free to post them below!
For full information about sources referenced in this series, please visit our LCIA Bibliography below.
For more information on impact assessment methods and models, please see the free online course on impact assessment and the free brown bag webinars offered by Earthshift Global:
Articles in This Series
Understanding Life Cycle Impact Assessment Process: Part I – Overview and Fate
Understanding Life Cycle Impact Assessment Process: Part II – Modeling Exposure: Intake and Bioavailability
Understanding Life Cycle Impact Assessment Process: Part III – Modeling Effects and Conclusion
About the Authors
Harnoor Dhaliwal is a certified LCA consultant at EarthShift Global. She holds a Bachelor’s degree in Botany from University of Delhi, India, and a Master’s degree in Environmental Policy Studies from New Jersey Institute of Technology. She did her graduate research work on sustainable remediation of contaminated sites. At EarthShift Global, Harnoor has carried out ISO-compliant Life Cycle Assessment studies on products including biofuels, packaging materials, food products, medical and pharmaceutical products, and industrial equipment. She has also developed and taught LCA courses. Her current focus is evaluating social Life Cycle Assessment and its application.
Pete Dunn, EarthShift Global’s marketing consultant is an entrepreneurial marketing and communications strategist and writer, serving clients in academia, technology and B-to-B marketing. His journalism background includes eight years as founder, editor and publisher of WaferNews, the leading news publication for the international semiconductor manufacturing community. He specializes in creative collaboration and translating complex subjects into clear messages that inform and inspire.
Goedkoop, M. and R. Spriensma (1999). The Eco-indicator 99. A damage oriented method for life cycle impact assessment. Methodology report and annex. Pré Consultants, Amersfoort, the Netherlands. http://www.pre.nl/eco-indicator99/
Hauschild M.Z., Huijbregts M., Jolliet O., Macleod M., Margni M., Dik van de Meent, Rosenbaum R.K., McKone T. E. (2008). Building a Model Based on Scientific Consensus for Life Cycle Impact Assessment of Chemicals: The Search for Harmony and Parsimony. Environmental Science &Technology, 2008, 42 (19), pp 7032–7037.
Huijbregts MAJ, Thissen U, Guinée JB, Jager T, Van de Meent D, Ragas AMJ, Wegener Sleeswijk A, Reijnders L. 2000. Priority assessment of toxic substances in life cycle assessment, I: Calculation of toxicity potentials for 181 substances with the nested multi-media fate, exposure and effects model USES-LCA. Chemosphere 41:541-573.
IPCC Fourth Assessment Report: Climate Change 2007 (AR4), 2007.
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. Environment International 69 (2014) 67–89.
Van Jaarsveld, JA. 1995. Modelling the long-term atmospheric behaviour of pollutants on various spatial scales. PhD thesis. University of Utrecht, Utrecht, The Netherlands.