For life support functions, too, this kind of interaction is to be anticipated. The interaction works both ways. Ideally, the mechanisms of interactions between the local area and the region should be part of the characterisation model. However, scientific data for a quantitative link are unlikely to be available. At the present time, only Köllner (2000) has attempted to assess the impact of a change in biodiversity in a local area on that of the surrounding region. Even some of the inventory-related issues have not yet been solved. For instance, there are still controversies surrounding the definitions of occupation and transformation and the feasibility of connecting these notions to a functional unit. 1. 2. 3. 4. 5. 6. Summary of review There is no single 'authorised' method for assessing the impacts of land use in terms of loss of biodiversity and loss of life support functions


1. 2. 3. 4. 5. 6. Summary of review There is no single 'authorised' method for assessing the impacts of land use in terms of loss of biodiversity and loss of life support functions. However, a conceptual scheme for land use impacts is currently being developed and debated (Lindeijer, 2000). There are two operational methods for assessing impacts on biodiversity (indicator: plant species density) due to land occupation and land transformation: Lindeijer et al. (1998) and Köllner (2000). There is one operational method for assessing impacts on life support functions (indicator: free Net Primary Production) due to land occupation and land transformation: Lindeijer et al. (1998). For both methods, assessment of the impact of occupation is not yet in line with the proposals of SETAC WIA–2. Only the assessment of the impact of transformation according to Lindeijer et al. (1998) is in line with the proposals made by SETAC WIA–2

Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002)


Only the assessment of the impact of transformation according to Lindeijer et al. (1998) is in line with the proposals made by SETAC WIA–2. Both methods are presently very limited in terms of the number of land use types that can be assessed; for other land use types characterisation factors must be developed on a case-by-case basis. Regionalisation is required to assess the biodiversity and possibly life support impacts of land use.


Part 3: Scientific background At present, only the method of Lindeijer et al. (1998) includes a first rough regionalisation of the reference values required for calculating the characterisation factors for biodiversity (plant species density) and life support functions (fNPP); Köllner's method is based on the Swiss lowlands and might, at best, be representative merely for central Europe. Regional data on actual, initial and final quality situations of land use, required for calculating the characterisation factors for biodiversity (plant species density) and life support functions (fNPP) are generally lacking and will have to be gathered on a case-by-case basis. The SETAC-WIA framework for land use and its impacts can only be considered as a first step in tying together the various different approaches. At present, it has insufficient conceptual clarity and has a number of inconsistencies


At present, it has insufficient conceptual clarity and has a number of inconsistencies. PROSPECTS Besides loss of biodiversity and loss of life support functions there are also other impacts that used to be filed under ‘land use’, such as landscape degeneration and desiccation. At the moment there are no known indicators for these impacts that might be used in LCA1. In 1999 a research project was started by RIZA and KIWA (NL) to study the possibility of incorporating an indicator for desiccation in LCA. A followup project to operationalise desiccation, using Lindeijer’s and/or Goedkoop & Spriensma’s method, is scheduled to start by mid–2000. A more extensive discussion on desiccation can be found in Section 4.3.4. Vascular plant species richness is used as a basic indicator for biodiversity impacts of land use. As mentioned in Lindeijer et al. (1998) and Köllner (2000) this implies that plant species are good indicators for total diversity; this may be inadequate for conservationists


As mentioned in Lindeijer et al. (1998) and Köllner (2000) this implies that plant species are good indicators for total diversity; this may be inadequate for conservationists. If data permit, this indicator might be weighted according to rare species or rare habitats. Eventually, vertebrate richness might also be included as birds and mammals are included in global biodiversity plans. However, this would probably lead to an adaptation of the formulae and weighting of vertebrates and plants would be required. Finally, impacts of occupation on biodiversity may be excluded for non-stressed areas if it can be proven that no significant biodiversity impacts occur due to mere occupation of land (i.e. with no change in quality) in these non-stressed areas. Species richness can be accompanied by other indicators for biodiversity changes to assess and state the total impact of land use on the natural environment


Species richness can be accompanied by other indicators for biodiversity changes to assess and state the total impact of land use on the natural environment. The biomass indicator for life support functions might also be accompanied by others, like soil quality. The functional approach (see under Developments in the last decade) could serve as a framework for this broadening of the set of indicators. However, clear links should be made to the endpoints for each indicator. This calls for quantification of the indicators, the endpoint and ideally the relationship between the two. This is not yet within the bounds of scientific knowledge. Again ideally, the relationship between biodiversity and life support functions should also be quantified. This is not currently feasible and, indeed, may never be. Finally, irreversibility of transformations should be considered in contrast to potential impacts due to occupation, taking into account different types of endpoint impacts


Finally, irreversibility of transformations should be considered in contrast to potential impacts due to occupation, taking into account different types of endpoint impacts. Another line of improvement is (more detailed) regionalisation. This relates not only to the reference state, but also to initial and final states of transformations. If regional changes in land use can be attributed to specific land use types, this would allow a generic inclusion of transformation data per region. Another route to gather transformation data is to collect more specific data for different land use types, as Lindeijer et al. (1998) have done. Regionalisation implies adding regional information to land use intervention data, for instance by specifying the process type. Finally, it may be queried whether the linear relationship between area, time and impact indicator is valid for all land use types


Finally, it may be queried whether the linear relationship between area, time and impact indicator is valid for all land use types. Including the area linearly is sometimes questioned because the area in question is used intentionally, with, in the case of forestry and agriculture, deliberate impacts on the environment (reducing biodiversity for the sake of productivity). This is different from impacts due to emissions. Also, biodiversity does not in fact vary linearly with area owing to the area-species relationships mentioned earlier. The assumption of linearity for non-critical areas may be adjusted in more refined modeling. 1 Early attempts to assess landscape degradation in terms of top-height (see Knoepfel, 1995) or above-ground biomass (see Lindeijer et al., 1998, p. A1.27) are not acceptable for all land use types (see Lindeijer et al., 1998, p. 20). For landscape degradation, the only possible indicator which seems to have some uniform value is landscape diversity


20). For landscape degradation, the only possible indicator which seems to have some uniform value is landscape diversity. This implies that only combinations of different types of land use can be assessed. Moreover, changes in one land use type within such a local combination of land uses, or occupation by this combination can only be assessed by means of panels. Combination of panel results from different regions to an overall LCA view on the landscape degradation seems impossible, both conceptually and practically.


Part 3 : Scientific background CONCLUSIONS It is not currently feasible to select a satisfactory baseline method for loss of biodiversity and life support functions. There are too many flaws attached to all the methods currently available. Therefore, no baseline method is recommended. However, for LCA studies in which land use impacts may play a significant or even dominant role it is advised (particularly in detailed LCA studies) to use the methods of Lindeijer (1998), Köllner (2000) and, if possible, an operationalisation of the SETAC conceptual scheme for land use impacts. Loss of biodiversity: characterisation method baseline alternative 1 alternative 2 additional variant characterisation model reference based on a statistical measure of species density Lindeijer et al


(1998) based on a statistical measure of plant species Köllner (2000) density Loss of life support functions: characterisation method baseline alternative additional diverging characterisation model reference based on Net Primary Production Lindeijer et al. (1998) RESEARCH RECOMMENDATIONS Short-term research It is highly recommended to start a project to arrive at one authorised set of methods for the inventory analysis and characterisation of the various impacts of land use. Long-term research The difference between occupation impacts and transformation impacts in relation to the endpoints requires closer scrutiny. To this end irreversible versus temporary impacts should be distinguished. Impacts of different agricultural and silvicultural management systems can be expressed in terms of occupation impacts but also as long-term transformations. A distinction between occupation and transformation in data and Impact assessment is required


A distinction between occupation and transformation in data and Impact assessment is required. The acceptability of finally aggregating might be refined, but transformation data related to trends in land use changes is even more important, considering the probably greater environmental relevance of transformations relative to occupation. In general, further development of the indicator set for land use impacts is required. Sophistication should be improved through greater use of regionalised data and, if necessary, more indicators, supported by adequate inventory data, also for reference state indicators. The relationship between area of land use and impact indicators should also be studied in greater detail. Finally, other interventions leading to similar impacts (such as desiccation and intersection) may be operationalised using the same Impact assessment approach, and the distinction made between occupation and transformation impacts needs a framework for Interpretation


Desiccation TOPIC Desiccation refers to a group of related environmental problems caused by water shortages due to groundwater extraction for industrial and potable water supply, enhanced drainage and water management (i.e. manipulation of the water table). This may lead to a lowered water table, reduced seepage, introduction of water from other areas and (consequently) changes in natural vegetation. The area of protection is the natural environment. 4.3.4


Part 3: Scientific background DEVELOPMENTS IN THE LAST DECADE Heijungs et al. (1992) In Heijungs et al.(1992) no indicator was recommended: “If the Inventory analysis yielded information about water use this could be totalled and used as a rough indicator for the issue of desiccation. Such a coarse approach, however, does not provide much more information about the actual issue...”. No useful methods have yet been developed for incorporating desiccation in LCA. PROSPECTS As mentioned earlier, RIZA and KIWA (NL) are currently examining the scope for incorporating a category indicator for desiccation in LCA. A follow-up project to operationalise desiccation using the method of either Lindeijer (1998) or Goedkoop & Spriensma (1999), or possibly both, is due to start by mid–2000. A research project on the topic is also under way in Australia. CONCLUSIONS No baseline method is recommended


A research project on the topic is also under way in Australia. CONCLUSIONS No baseline method is recommended. method status baseline alternative additional variant characterisation method/factor _ _ – reference _ _ _ – RESEARCH RECOMMENDATIONS No further research recommended. 4.3.5 Climate change TOPIC Climate change is defined here as the impact of human emissions on the radiative forcing (i.e. heat radiation absorption) of the atmosphere. This may in turn have adverse impacts on ecosystem health, human health and material welfare. Most of these emissions enhance radiative forcing, causing the temperature at the earth’s surface to rise. This is popularly referred to as the ‘greenhouse effect’. The areas of protection are human health, the natural environment and the man-made environment (see Figure 4.2.2). DEVELOPMENTS IN THE LAST DECADE Heijungs et al. (1992) In Heijungs et al


The areas of protection are human health, the natural environment and the man-made environment (see Figure 4.2.2). DEVELOPMENTS IN THE LAST DECADE Heijungs et al. (1992) In Heijungs et al. (1992) Global Warming Potentials (GWPs) were used as characterisation factors to assess and aggregate the interventions for the impact category climate change (there termed “enhanced greenhouse effect”): where is the mass of substance i released in kg, the Global Warming Potential of the substance and Climate Change the indicator result, which is expressed in kg To compare the impacts of emissions of different greenhouse gases, each has been assigned a socalled Global Warming Potential (GWP) index, expressing the ratio between the increased infrared absorption due to the instantaneous emission of 1 kg of the substance and that due to an equal emission of carbon dioxide both integrated over time:


Part 3 : Scientific background with: the radiative forcing per unit concentration increase of greenhouse gas i the concentration of greenhouse gas i at time t after the release and the time over which integration is performed (yr). T The corresponding values for carbon dioxide are included in the denominator of the equation (Houghton et al., 1991; Jäger & Ferguson , 1991). GWP is a measure of the potential contribution a substance to climate change and incorporates considerations of fate. It merely provides a rough indication of the potential climatic effects of such emissions, as these depend not only on integrated atmospheric heat absorption but also on its distribution over time. This integration of the process of global warming involves a number of simplifications. In particular, GWPs depend on the time horizon T to which integration is performed


This integration of the process of global warming involves a number of simplifications. In particular, GWPs depend on the time horizon T to which integration is performed. Longer horizons (100 and 500 years) are used to assess the cumulative effect of greenhouse gas emissions, while shorter horizons (20 and 50 years) provide an indication of short-term effects. The longer the time horizon the more unreliable GWPs become, as their value is determined by the background concentration of other components of the atmosphere. Although these concentrations are assumed to remain constant in time (Houghton et al., 1992), they are likely to change. If assumptions about background concentrations change, GWPs also change. For example, the life span of substances eliminated by OH-radicals in the atmosphere may change significantly, depending on future changes in anthropogenic emissions of methane, carbon monoxide and nitrogen oxides (Houghton et al., 1991; Jäger & Ferguson, 1991)


Such changes in life span have a major, often disproportional, effect on the GWPs of all the substances concerned, as these are defined in relative terms. The Intergovernmental Panel on Climate Change (IPCC) has compiled a list of ‘provisional best estimates’ for GWPs with time horizons of 20, 100 and 500 years, based on the expert judgement of scientists worldwide. This list of GWPs is periodically updated. The GWPs used in Heijungs et al. (1992) are based on the 1992 IPCC list (Houghton et al., 1992). The integration period to be applied in LCA calculations must be decided on by the practitioner and depends on the period over which the impacts are to be studied. A long horizon would appear to be preferable for the characterisation step of LCIA, as the aim of LCA is to assess all rather than just shortterm effects. As stated, however, the longer the integration period, the more uncertainties are introduced into the model. Hence, Heijungs et al


As stated, however, the longer the integration period, the more uncertainties are introduced into the model. Hence, Heijungs et al. (1992) recommended using all three IPCC time horizons. In practice this means that integration is first performed for 100 years, say, and the uncertainty margins in the result then determined by performing parallel calculations for the other two integration periods. As mentioned, the IPCC’s list of GWPs is periodically updated, for the first time in 1994 (Houghton et al., 1994) and, for selected substances, again in 1996 (Houghton et al., 1996). In this last update, a net GWP for ozone-depleting gases was discussed. While these gases absorb infrared radiation and thus increase radiative forcing, this is offset to some extent by a decrease in forcing due to the loss of stratospheric ozone. For these gases, therefore, a net GWP would be more appropriate than one based solely on direct impact. The net GWPs given in Houghton et al


For these gases, therefore, a net GWP would be more appropriate than one based solely on direct impact. The net GWPs given in Houghton et al. (1996) show that while some ozone-depleting substances, like CFCs, still have a positive net GWP, for others, such as halons, this figure is negative. It is to be queried whether ozone layer depletion should be regarded as a ‘positive’ effect. Because negative impacts on the ozone layer are accounted for under the impact category ‘depletion of stratospheric ozone’, however, it seems appropriate to do so. At the same time, though, the net GWPs calculated to date are “subject to considerable quantitative uncertainties (at least 50%)” (Houghton et al., 1996). PROSPECTS In the future, further progress is anticipated on Impact assessment modeling to the various endpoints, viz. damage to human health, damage to ecosystem health, etc. (cf. Figure 4.2.2); see Section 4.2 for a more extensive discussion of this subject).


Part 3: Scientific background CONCLUSIONS Our conclusion is that the GWP approach, using the most recent indices published by the IPCC (Houghton et al., 1994 and 1996), is currently the best available practice. Because of the uncertainties in net GWPs for ozone-depleting gases, these indices have not been included in the baseline method (criterion 12 for the selection of baseline characterisation methods). If these uncertainties can be narrowed down in further research, net GWPs should be used for ozone-depleting gases, as these are a more accurate reflection of our current understanding of relevant environmental mechanisms (criterion 1). The GWPs for 100 years are recommended as the baseline characterisation method for climate change. The IPCC also provides GWPs for 20 and 500 years. Although 500 years is closer to eternity (criterion 7), we do not recommend using the GWPs for 500 years as the baseline, because of growing uncertainties in GWP with increasing time span (criterion 12)


The baseline characterisation method for climate change, using the characterisation factor is evaluated with respect to the (ISO-based) criteria adopted in this Guide in Table 4.3.5.1. Table 4.3.5.1: Evaluation of the baseline characterisation method for climate change, using the characterisation factor with respect to the (ISO-based) criteria of Table 4.3.1. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12


1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. criterion scientifically and technically valid environmentallyrelevant internationally accepted value-choices and assumptions focal point in environmental mechanism linearity timespan fate, exposure/intake and effects less is better time- and location-independent operational uncertainty margins evaluation yes, calculation of the contributions of greenhouse gases to radiative forcing is based on well understood environmental processes yes, there is international agreement that climate change induced by radiative forcing will have environmental impacts such as sea level rise, destruction of coastal ecosystems, depressed crop yields, etc. yes, supported by the Intergovernmental Panel on Climate Change (IPCC) yes, but agreed upon by an authoritative international body(IPCC)


yes, supported by the Intergovernmental Panel on Climate Change (IPCC) yes, but agreed upon by an authoritative international body(IPCC). midpoint yes 100 years, not eternity fate included, exposure/intake not relevant, effects included in terms of effects on radiative forcing yes, no threshold yes yes known and accepted by IPCC The following method is included in this Guide as an option for sensitivity analyses: The GWPs for 20 and 500 years can be used to explore the consequences of adopting different time horizons. Recommendations for extended LCAs: For several ozone-depleting gases, the upper and lower limits of the uncertainty range of net GWPs may be useful in an extended LCA


Recommendations for extended LCAs: For several ozone-depleting gases, the upper and lower limits of the uncertainty range of net GWPs may be useful in an extended LCA. method status baseline alternative 1 alternative 2 alternative 3 alternative 4 additional variant characterisation method/factor upper limit of net GWP lower limit of net GWP – – reference Houghton et al., 1994, 1996 Houghton et al., 1994, 1996 Houghton et al., 1994, 1996 Houghtonetal., 1996 Houghton et al., 1996 – – RESEARCH RECOMMENDATIONS Given the ongoing work of the IPCC, no further research is recommended.


Part 3 : Scientific background 4.3.6 Stratospheric ozone depletion TOPIC Stratospheric ozone depletion refers to the thinning of the stratospheric ozone layer as a result of anthropogenic emissions. This causes a greater fraction of solar UV-B radiation to reach the earth’s surface, with potentially harmful impacts on human health, animal health, terrestrial and aquatic ecosystems, biochemical cycles and materials (UNEP, 1998). Stratospheric ozone depletion thus impinges on all four areas of protection: human health, the natural environment, the man-made environment and natural resources (see Figure 4.2.2). DEVELOPMENTS IN THE LAST DECADE The concept of Ozone Depletion Potential (ODP) was introduced by Wuebbles (1988). The ODP of a Heijungs et al. (1992) In Heijungs et al


DEVELOPMENTS IN THE LAST DECADE The concept of Ozone Depletion Potential (ODP) was introduced by Wuebbles (1988). The ODP of a Heijungs et al. (1992) In Heijungs et al. (1992) Ozone Depletion Potentials (ODPs) were used as a characterisation factor to assess and aggregate the interventions for the impact category stratospheric ozone depletion: where is the mass of substance i released, the Ozone Depletion Potential of the substance and Ozone Depletion the indicator result, which is expressed in kg CFC–11-equivalents. substance is defined as follows: with: represents the change in the stratospheric ozone column i the equilibrium state due to annual emissions of substance i (flux in and the change in this column in the equilibrium state due to annual emissions of CFC–11. It can be shown that an ODP based on an emission flux also provides a good indication of the relative changes in the ozone column due to an instantaneous emission (kg) to the atmosphere (WMO, 1989)


It can be shown that an ODP based on an emission flux also provides a good indication of the relative changes in the ozone column due to an instantaneous emission (kg) to the atmosphere (WMO, 1989). Although the ODP concept resembles that of GWP, there is a major difference: ODPs are calculated for a steady state, GWPs for several different time horizons. The World Meteorological Organisation (WMO) has compiled a list of ‘best estimates’ for ODPs, first published in 1992 (WMO, 1992). The report was compiled by the Scientific Assessment Panel, comprising the relevant authorities in the field, and it is therefore reasonable to assume that the results enjoy wide international support. The ODPs used in Heijungs et al. (1992) are based on this original list, which the WMO updated in 1995 (WMO, 1995) and again in 1999 (WMO, 1999). These ODPs are steady-state ODPs based on a model. They describe the integrated impact of an emission of a substance on the ozone layer compared with CFC–11


These ODPs are steady-state ODPs based on a model. They describe the integrated impact of an emission of a substance on the ozone layer compared with CFC–11. These model-derived ODPs are recommended by Nichols et al. (1996) and by Hauschild & Wenzel (1998) for situations in which the time span of interest is eternity. In other cases Nichols et al. (1996) and WMO (1995) recommend using the time-dependent ODPs given by Solomon & Albritton (1992). These time-dependent ODPs are based on an empirical approach, viz. on measurements on the lower layers of the stratosphere. Hauschild & Wenzel (1998) also suggest that the time-dependent ODPs might be better for LCAs with a shorter horizon. For the time being, however, they opt to use the steady-state ODPs in their EDIP methodology. Lindfors (1996) has evaluated the major impact categories for LCA within the framework of the EU ecolabeling programme


Lindfors (1996) has evaluated the major impact categories for LCA within the framework of the EU ecolabeling programme. He draws a different conclusion from Nichols et al.(1996) and Hauschild & Wenzel (1998), stating that the category of stratospheric ozone depletion will become less important in the future because of the ongoing phase-out of ozone-depleting chemicals. He recommends not using ODPs, because of all the uncertainties involved (mainly data gaps in the inventory). Instead he proposes classifying and/or flagging emissions of ozone-depleting chemicals or use of ozone-depleting chemicals in products or technologies, with no further characterisation. It should be borne in mind, however, that this is specifically for the underpinning of ecolabeling criteria. For LCIA in general, using the best available method to characterise stratospheric ozone depletion seems to be a better approach than using no


Part 3: Scientific background method at all. It is questionable, furthermore, whether emissions of ozone-depleting are set to decline at the global level, let alone stratospheric concentrations. PROSPECTS In the future, further progress is anticipated on Impact assessment modeling to the various endpoints, viz. damage to human health, damage to ecosystem health, etc. (cf. Figure 4.2.2); see Section 4.2 for a more extensive discussion of this subject). CONCLUSIONS As a default for both simplified and detailed LCAs it is recommended to use the most recent steady-state ODPs published by the WMO (1999), augmented where necessary by ODPs from the original 1992 list (WMO, 1992)


These values are internationally accepted (criterion 3 for selection of baseline characterisation method)andasteady-stateODPapproximatesanODPfor The baseline characterisation method forstratospheric ozonedepletion, usingthecharacterisation factor is evaluated with respect to the (ISO-based) criteria adopted in this Guide in Table 4.3.6.1. Table 4.3.6.1: Evaluation of the baseline characterisation method for stratospheric ozone depletion, using the characterisation factor with respect to the (ISO-based) criteria of Table 4.3.1. 1. 2. 3. 4. 6. 7. 8. 9. 10. 11. 12


1. 2. 3. 4. 6. 7. 8. 9. 10. 11. 12. criterion scientifically and technically valid environmentally relevant internationally accepted value-choices and assumptions focal point linearity time span fate, exposure/intake and effects less is better time- and location-independent operational uncertainty margins evaluation yes, calculation of stratospheric ozone depletion is based on well understood environmental processes yes, there is international agreement that stratospheric ozone depletion results in an increase of UV-B intensity, causing a variety of radiation effects on humans, algae, arctic flora, crops, etc. yes, supported by the World Meteorological Organisation (WMO) and the United Nations yes, but agreed on by an authoritative international body (WMO)


yes, supported by the World Meteorological Organisation (WMO) and the United Nations yes, but agreed on by an authoritative international body (WMO). midpoint yes eternity fate included, exposure/intake not relevant, effects included in terms of effects on stratospheric ozone layer yes, no threshold yes yes known and accepted by WMO The following method is included in this Guide as an option forsensitivityanalyses: The time-dependent ODPs of Solomon & Albritton (1992) can be used to explore the consequences of adoptingdifferent timehorizons. No additional methods are recommended for extended LCAs


No additional methods are recommended for extended LCAs. method status baseline alternative 1 alternative 2 alternative 3 alternative 4 alternative 5 alternative 6 alternative 7 additional variant characterisation method/factor – – reference WMO, 1992; WMO, 1995; WMO ,1999 Solomon & Albritton, 1992 Solomon & Albritton, 1992 Solomon & Albritton, 1992 Solomon & Albritton, 1992 Solomon & Albritton, 1992 Solomon & Albritton, 1992 Solomon & Albritton, 1992 – – RESEARCH RECOMMENDATIONS Lona-term research: Research is recommended to identify the differences and similarities between the methods used to deriveODPsandGWPs.


Part 3 : Scientific background 4.3.7 Human toxicity TOPIC This impact category covers the impacts on human health of toxic substances present in the environment. The health risks of exposure in the workplace are also sometimes included in LCA (see, for example, Lindfors et al., 1995c; Hauschild & Wenzel, 1998; Schmidt & Brunn Rasmussen, 1999). These latter risks are often included in a wider impact category encompassing more than exposure to toxic substances (e.g. accidents at work). Schmidt & Brunn Rasmussen (1999) describe a useful method for including the working environment in LCA, based on a database developed by EDIP in which workplace impacts per kilogram of produced goods are listed for a number of economic activities. However, the scope of the present project precluded assessment of whether this method could be adapted and incorporated in this Guide. No further consideration is therefore given here to the impacts of exposure to toxic substances in the workplace


No further consideration is therefore given here to the impacts of exposure to toxic substances in the workplace. The area of protection for this impact category is human health (see Figure 4.2.2). A variety of characterisation methods have been developed for the impact category human toxicity providing characterisation factors that are generally referred to as ‘human toxicity potentials’ (HTPs). As described by Heijungs & Wegener Sleeswijk (1999) the general formula for calculating the HTP embraces three independent dimensions: fate, exposure/intake and effect. Here, we add a fourth dimension to account for transfer: with: the Human Toxicity Potential, the characterisation factor for the human toxicity of substance i emitted to emission compartment ecomp. In some methods the contributions via exposure routes r are not summed, yielding several HTPs


In some methods the contributions via exposure routes r are not summed, yielding several HTPs. a ‘fate factor’, representing intermedia transport of substance i from emission compartment ecomp to final (sub)compartment fcomp, and degradation within compartment ecomp; in some methods intermedia transport is indicated separately by and (bio)degradation by the transfer factor, the fraction of substance i transferrred from fcomp to exposure route r, i.e. air, drinking water, fish, plants, meat, milk, etc.; an ‘intake factor’, representing human intake via exposure route r, thus, a function of daily intake of air, drinking water, fish, etc.; an ‘effect factor’, representing the toxic effect of intake of substance via exposure route r. The HTP is often defined relative to a reference substance. As a formula: with the symbols similar to the above1. The choice of the reference substance is arbitrary


The HTP is often defined relative to a reference substance. As a formula: with the symbols similar to the above1. The choice of the reference substance is arbitrary. This general description of the HTP-formula and the associated terminology provides an initial handle for describing and discussing the various characterisation methods developed for the impact category human toxicity. Thus, the terms used in this Guide to describe these methods may differ from those employed by the authors in question. 1 The HTP using a reference substance is dimensionless or has the unit ‘kg reference substance· of substance In this section and the following it is considered as a dimensionless quantity, although in Part 2b it is precisely quantified.


Part 3: Scientific background A second point of terminology concerns the environmental ‘compartments’ or ‘media’, used for the three basic subdivisions air, water and soil. In this Guide ‘compartment’ is used as the preferred term, except in the standard collocations ‘multimedia’ and ‘intermedia’. ‘Subcompartments’ are subdivisions of air, water or soil that are in chemical equilibrium with the compartment of which they are a part. Examples of subcompartments are aerosols, suspended matter and pore water in soils. Compartments or Subcompartments through which humans are exposed to a toxic substance are referred to as exposure routes (see textbox for some further details). General structure of models for toxicity assessment The impact categories relating to toxicity-oriented problems can be described in terms of the main aspects covered (see also description under Topic). These aspects are: 1. Fate


These aspects are: 1. Fate. The residence time of a chemical in a particular environmental compartment depends on degradation mechanisms and transport processes, e.g. from air to soil by rain, from water to air by evaporation, from soil to water by run-off. 2. Transfer. The fraction of a substance transferred from a given compartment to an exposure route, i.e. air, drinking water, fish, plants, meat, milk. 3. Exposure/intake. The intake of a given chemical by an organism depends on its food pattern, water intake and respiratory volume. 4. Effect. There is wide variation in the hazards posed by chemicals; dioxins, for example, are more toxic than nitrates. Fate, transfer and exposure/intake are often modeled together. A number of compartments and subcompartments are distinguished, e.g. air, soil, freshwater, marine waters and sediment. Environmental processes like rainfall, degradation, sedimentation and immobilisation (e.g


air, soil, freshwater, marine waters and sediment. Environmental processes like rainfall, degradation, sedimentation and immobilisation (e.g. by burial in deeper sediments) are captured in model equations, which are then extended via exposure routes (air, drinking water, crops, meat, milk, fish) to target organisms, including humans, or to target ecosystems (terrestrial, freshwater, marine). As noted in Section 1.2.3.3 indirect exposure to humans via all manner of foodstuffs is a very significant exposure route for many substances. Exposure by this route depends on foodstuff consumption as such, but also on bioconcentration and biomagnification in all these foodstuffs, which are derived directly or indirectly from plants, including meat and dairy. Because of the enormous variety of foodstuffs we consume, the soil route is by far the most complex route for transfer modeling. Most fate, transfer and exposure/intake models are steady-state models, i.e


Most fate, transfer and exposure/intake models are steady-state models, i.e. they calculate a concentration or intake level due to a constant emission rate. Dynamic models, on the other hand, yield a pattern in time, as the result of a constant, varying or pulse emission input. Timeintegration then condenses such a pattern into a single result. Steady-state models are also applied to assess emission pulses. It can be shown that most steady-state models based on an emission flux also provide a good indication of the relative changes in fate and exposure/intake due to an emission flux (kg) (Guinée et al., 1996). Most fate models assume homogenous mixing, but occasionally lagrangian or gaussian models are used to calculate concentration gradients. Space-integration may then also be required. Homogenous box-models may allow for discrete regional differences as well, e.g. at the scale of continents, climatic zones or countries


Space-integration may then also be required. Homogenous box-models may allow for discrete regional differences as well, e.g. at the scale of continents, climatic zones or countries. Space-integration in that case amounts to volume-weighted addition. The effect measure is often based on toxicologically-based yardsticks such as EC50, defined as the concentration at which 50% of the target organisms shows an effect. Extrapolation or safety factors may be applied to convert the results from the laboratory to the field, from rat to man, from single species to ecosystem, etc, yielding NOECs (No Observed Effect concentration), ADIs (Acceptable Daily Intakes), TDIs (Tolerable Daily Intakes), PNECs (Predicted No-Effect Concentrations), MTCs (Maximum Tolerable Concentrations), NOAELs (No Observed Adverse Effect Levels) and so on. The terms ‘acceptable’, ‘tolerable’ and ‘adverse’ imply that these measures are mainly based on toxicological (rather than economic, political, etc.) considerations.


Part 3 : Scientific background DEVELOPMENTS IN THE LAST DECADE Human toxicological impact, together with ecotoxicological impact, is the impact category for which fate Heijungs et al. (1992) Heijungs et al. (1992) defined separate characterisation factors for emissions of toxic substances to the environmental compartments air, water and soil: where HCA, HCW and HCS are the characterisation factors for human toxicological impacts resulting from emissions to air, water and soil, respectively (kg body substance). and are the daily intakes of air and water per person and 2 I waterW is the world population and are the volumes of air, water and soil in the world air, water and kg dry soil). ADI is the Acceptable Daily Intake: for substances with a threshold value (i.e


ADI is the Acceptable Daily Intake: for substances with a threshold value (i.e. an environmental concentration or intake value below which no harmful effects have been observed in humans, plants or animals) it is the daily intake that can be sustained life-long without adverse effects; for substances with no such threshold, it is the daily intake resulting in a risk of 1 extra case of cancer per 1000 life-long exposures (VROM, 1989). ADI is expressed as kg body weight. N is the uncertainty factor for the ADI and Cvalue is a former Dutch standard for soil (kg The indicator results for these media can be added without weighting to provide a single, medium-independent indicator result for human toxicity: where are the emissions of substance i to air, water and soil. Heijungs et al. stress that these characterisation factors for human toxicity should be considered as no more than indicative until such time as a better method is developed


Heijungs et al. stress that these characterisation factors for human toxicity should be considered as no more than indicative until such time as a better method is developed. and especially degradation and intermedia transport are most important. Organic substances, in particular, are generally degraded to yield other compounds that are less toxic than the substance originally emitted (although the opposite may also be true). Furthermore, substances do not generally remain in the environmental compartment into which they are emitted, but tend to spread to other (sub- )compartments, where they may do more damage. A volatile toxic substance discharged into waterways will evaporate largely to the atmosphere, for example, where it may expose humans to (severe) risk via the respiratory route. Many authors therefore stress that it is essential to properly incorporate degradation and intermedia transport in LCA models of human toxicological impact (e.g


Many authors therefore stress that it is essential to properly incorporate degradation and intermedia transport in LCA models of human toxicological impact (e.g. Heijungs et al., 1992; Lindfors et al., 1995c; Udo de Haes et al, 1996; Guinée et al., 1996; Jolliet & Crettaz, 1997; Hauschild & Wenzel, 1998). In line with these recommendations, we here consider only methods that include degradation and intermedia transport, which supersede the provisional method developed by Heijungs et al. (1992)1. (See text box.) Several such methods are described in the literature, falling roughly into three groups: methods in which degradation and intermedia transport is based on simple rules of thumb (e.g. Hauschild & Wenzel, 1998) methods in which degradation and intermedia transport is based on models (e.g. Guinée et al., 1996; Huijbregts, 1999a; Hertwich, 1999) methods in which degradation and intermedia transport is based on models and empirical relations (e.g. Jolliet & Crettaz, 1997). 1. 2. 3


Guinée et al., 1996; Huijbregts, 1999a; Hertwich, 1999) methods in which degradation and intermedia transport is based on models and empirical relations (e.g. Jolliet & Crettaz, 1997). 1. 2. 3. 1 Heijungs et al. stressed in 1992, already, that fate should be included; at the time, however, this could be done only provisionally.


Part 3: Scientific background Below, these methods are discussed in terms of a common notation as described above. For instance, F is used consistently to denote a fate factor, although a variety of symbols and terms are encountered in the literature. Moreover, the precise operationalisation and, consequently, the units used for broadly similar variables may differ from method to method. 1. Methods based on simple rules of thumb Hauschild & Wenzel (1998) have developed characterisation factors based on rules of thumb to model degradation and intermedia transport: with: the Human Toxicity Potential, the characterisation factor for the human toxicity of substance i emitted to emission compartment ecomp and leading to exposure via route r (e.g. fish or milk; see Figure 4.3.7.1). The emission compartments considered are air, water and soil, resulting in nine different HTPs per substance


fish or milk; see Figure 4.3.7.1). The emission compartments considered are air, water and soil, resulting in nine different HTPs per substance. Groundwater has been suggested as a fourth compartment, but no characterisation factors have yet been developed; the intermedia transport factor, the fraction of substance i emitted to emission compartment ecomp that reaches final compartment fcomp as a result of environmental transport. This factor is based on simple rules of thumb rather than on a full fate model. Moreover, it is not a continuous variable but assumes only a limited number of values such as 0.2 and 1; the biodegradability factor, to be chosen from one of three classes depending on the substance i involved; the transfer factor, the fraction of substance i transferrred from fcomp to exposure route r, i.e. air, fish, plants, animals, etc. (see Figure 4.3.7.1). In the case of soil the HTP includes only those exposure routes making the greatest contribution (i.e


air, fish, plants, animals, etc. (see Figure 4.3.7.1). In the case of soil the HTP includes only those exposure routes making the greatest contribution (i.e. with the highest the intake factor, the fraction of substance i taken up per kg body weight per day via exposure route r. There is a specific intake factor for each of the seven sub-compartments identified; the effect factor, representing the human-toxic impact of substance i via exposure route r; depending on the exposure route concerned, it is either the reciprocal of the Acceptable Daily Intake or that of the atmospheric concentration not anticipated to have toxic effects after life-long inhalation. 1 Hauschild & Wenzel use a different term for HTP, viz. ‘Equivalency Factor’. Moreover, Hauschild & Wenzel use the term ‘potential’ for the associated indicator results, whereas in this Guide the term is used for the characterisation factor


‘Equivalency Factor’. Moreover, Hauschild & Wenzel use the term ‘potential’ for the associated indicator results, whereas in this Guide the term is used for the characterisation factor. Finally, r is used here to indicate exposure routes such as ‘air’, ‘fish’ and so on (see Figure 4.3.7.1 and Figure 4.3.7.2), whereas Hauschild & Wenzel indicate these by abbreviations based on media: a = air, viz. respiration; w = water, viz. fish; s = soil, viz. crops (plants in Figure 4.3.7.1), cattle meat (animals in Figure 4.3.7.1) and dairy products (milk in Figure 4.3.7.1).


Part 3 : Scientific background The method of Hauschild & Wenzel (1998) yields three separate indicator results for human toxicity, one for each of the three principal exposure routes (air; fish; and soil, plants, animals and milk): where is as defined above and is the emission of substance i to compartment ecomp. These separate results are not aggregated to a single indicator result. Overall, it can be concluded that the method of Hauschild & Wenzel (1998) models degradation and intermedia transport only very rudimentarily. 2. Model-based methods Guinée et al. (1996) have developed characterisation factors for human toxicity including degradation and intermedia transport using the Uniform System for the Evaluation of Substances model, USES 1.0 (RIVM et al., 1994), which incorporates the multimedia model Simplebox 1.0 (Van de Meent, 1993) as a separate module


Simplebox calculates the Predicted Environmental Concentration (PEC) in four environmental compartments, represented as ‘boxes’: air, water, agricultural soil and industrial soil (i.e. soil used for agricultural and industrial purposes) due to a constant emission flux to any of these four compartments. For human toxicity, six exposure routes are assessed, viz. air, fish, drinking water, crops, cattle meat and milk (see Figure 4.3.7.2).


Part 3: Scientific background Although exposure route and intake are still modeled in a fairly rudimentary fashion, using similar methods to Hauschild & Wenzel (1998), Simplebox allows substance fate to be modeled rather more realistically and comprehensively, including both degradation and immobilisation and making use of continuous functions that depend on the chemical of interest rather than the constant factors f and BIO. A second major difference in the method of Guinée et al. is that the daily intakes accruing via the respective exposure routes are summed to yield a total Predicted Daily Intake (PDI)1, which is then divided by the Acceptable Daily Intake (ADI), expressed here once more as its reciprocal Finally, the score is divided by the score for an emission, to air, of a reference substance: 1,4 dichlorobenzene: with: the Human Toxicity Potential due to emission of 1000 kg of substance i per day (flux) to emission compartment ecom


The compartments considered are air, water and agricultural and industrial soil, resulting in four different HTPs per substance; the Predicted Daily Intake of substance i emitted to emission compartment ecomp; note that does not differ for different exposure routes since in this method does not differ among routes; the effect factor, representing the human-toxic impact of substance i, here the reciprocal of the ADI of the substance; the Predicted Daily Intake resulting from the emission of 1000 kg of 1,4dichlorobenzene per day to air; the effect factor for 1,4-dichlorobenzene, representing the human-toxic impact of 1,4-dichlorobenzene, here the reciprocal of the ADI of 1,4dichlorobenzene. It can be shown that an HTP based on an emission flux also provides a good indication of the relative human-toxic impact of an instantaneous emission (kg) (Guinée et al., 1996). The method of Guinée et al. (1996) results in a single indicator result for human toxicity: where and are as defined above


The method of Guinée et al. (1996) results in a single indicator result for human toxicity: where and are as defined above. is this a combination of and


Part 3 : Scientific background Equation (4.3.7.10) can also be written as: where r1 is exposure route 1, e.g. air, r2 exposure route 2, e.g. fish, and so on. Equation (4.3.7.11) clearly shows the difference from the method of Hauschild & Wenzel (1998), which yields separate indicator results for each of the exposure routes distinguished rather than their sum, here. Huijbregts (1999a) used a new version of the USES model, USES 2.01 (RIVM et al., 1998), and modified it to calculate new characterisation factors for human toxicity (as well as for aquatic, sediment and terrestrial ecotoxicity), using the same basic method as Guinée et al. (1996). The USES-LCA model thereby created (largely based on USES 2.0) improves on USES 1.0 in four main ways. In the first place, the fate of substances can now be modeled at the global level


(1996). The USES-LCA model thereby created (largely based on USES 2.0) improves on USES 1.0 in four main ways. In the first place, the fate of substances can now be modeled at the global level. USES 2.0 and USES-LCA have five spatial scales: ‘regional’, ‘continental’ and ‘global’, the last tripartite to reflect the arctic, temperate and tropical climate zones of the Northern hemisphere. The regional and continental scales each comprise six compartments: air, freshwater, seawater, natural soil, agricultural soil and industrial soil. The three climate zones of the global scale each comprise three compartments: air, (sea)water and soil. The global scale is modeled as a closed system with no transport out of the system (i.e. into space); emitted substances cannot therefore leave the system, as was the case in USES 1.0. Second, USES-LCA takes into account the temperature dependence of physico-chemical properties. Third, the variation of these properties with soil depth is also modeled


Second, USES-LCA takes into account the temperature dependence of physico-chemical properties. Third, the variation of these properties with soil depth is also modeled. Finally, the continental-scale model now has separate freshwater and seawater compartments. Given the differences between the USES-LCA and USES 1.0 models, Huijbregts (1999a) also differed from Guinée et al. (1996) in a number of choices. The principal of these derives from the fact that USESLCA is a nested model, with five scale, as described above. USES 1.0, in contrast, had only a single, continental scale2


The principal of these derives from the fact that USESLCA is a nested model, with five scale, as described above. USES 1.0, in contrast, had only a single, continental scale2. To calculate a single characterisation factor for each emission compartment, Huijbregts therefore aggregated the four factors calculated at the global and continental scales on a population basis: the larger the exposed population, the greater the weight of the associated factor: with: the Human Toxicity Potential of substance i emitted to emission compartment ecomp (dimensionless); the population density at scale s; the Predicted Daily Intake via. exposure route r at scale s for substance i emitted to emission compartment ecomp the effect factor, representing the human-toxic impact of substance i, here the Acceptable Daily Intake via exposure route r (inhalation or ingestion) (day)


Although USES-LCA models fate and exposure routes more realistically and comprehensively than earlier methods, using continuous variables, the toxicity potentials thus calculated still embody areas of major uncertainty. The sensitivity of the USES-LCA model to (value) choices it embodies also needs to be carefully assessed. . The main value choices concern the temporal and spatial horizons adopted to calculate HTP. In the case of the impact category ‘climate change’, the value of the characterisation factor GWP may vary by more than an order of magnitude, depending on the time horizon chosen (Houghton et al., 1996). In the case of toxicity potentials, an infinite time horizon has generally been adopted (Guinée et al., 1996; Hertwich et al., 1998 & 1999; Huijbregts 1999a; Huijbregts, 2000). In Impact assessment this approach may obscure the potential shorter-term impacts of product systems, however


In Impact assessment this approach may obscure the potential shorter-term impacts of product systems, however. With respect to spatial horizon, the basic choice concerns whether or not HTP includes the potential impacts exported from the continental to the global scale. As mentioned, Huijbregts (2000) 1 USES 2.0 is an adapted version of EUSES, in turn an upgraded and updated European version of USES 1.0.. 2 Although both USES 1.0 and 2.0 permit calculation on a local and regional scale, these scales were not taken into account in calculating characterisation factors.


Part 3: Scientific background includes the latter using scale-specific, population-based weighting factors. However, including globalscale impacts may obscure potential impacts at the continental scale. Huijbregts therefore ran a number of scenarios to assess the influence of these choices (Huijbregts, 2000). Toxicity potentials were calculated for horizons of 20, 100 and 500 years by integrating the amount of a substance present in compartment fcomp after an emission pulse released to compartment ecomp over the respective periods and compared with the value previously obtained by integration to infinity (Figure 4.3.7.3 and Figure 4.3.7.4). These three horizons are the same as those used to calculate Global Warming Potentials (Houghton et al., 1996) and appear to provide a practicable range for policy applications. The sensitivity of the model to the choice of spatial horizon was assessed by comparing toxicity potentials calculated with and without inclusion of the global scale


The sensitivity of the model to the choice of spatial horizon was assessed by comparing toxicity potentials calculated with and without inclusion of the global scale. To this end Huijbregts (2000) aggregated potential impacts at the continental scale and each of the three zones of the global scale using scalespecific weighting factors. Potential impacts in the marine aquatic compartments were aggregated on the basis of compartment volume, impacts in the marine sediment and terrestrial compartments on the basis of compartment mass. For human toxicity, the human population at the scale level in question was used as a weighting factor. For the impact categories associated with the freshwater aquatic and sediment compartments no weighting factors were required, as these compartments are identified at the continental scale only. In calculating toxicity potentials, potential global-scale impacts can be excluded by


Part 3 : Scientific background assigning a zero value to the weighting factors for the contributions of arctic, temperate and tropical zones to the impact categories involved. The results of the time horizon scenario analyses (see Part 2b, Section 4.3.8, and see Huijbregts, 2000) show differences of up to 6.5 orders of magnitude for the toxicity potentials of the metals studied, while for organic chemicals these differences remain within half an order of magnitude. In terms of the LCA result for the impact category human toxicity, this means that the longer the time horizon, the more dominant the very persistent heavy metals become. Scenario analyses addressing the extent to which inclusion of global-scale impacts obscures potential continental-scale impacts indicate differences in the toxicity potentials of metals and volatile persistent halogenated organics of up to 2.3 orders of magnitude. For a more extensive discussion of these results, we refer to Huijbregts (2000)


For a more extensive discussion of these results, we refer to Huijbregts (2000). The use of time horizon-specific HTPs in LCAs is relatively straightforward. The indicator result for human toxicity and a specified time horizon can be calculated using the formula: with: the indicator result for human toxicity for time horizon t (kg); the Human Toxicity Potential of substance i emitted to emission compartment ecomp for time horizon t (dimensionless); the emission of substance i to compartment ecomp (kg). As metals and volatile persistent halogenated organics may be responsible for a substantial share of the potential human-toxic impact of product systems, the (value) choice of temporal and spatial horizon is particulartly important in LCA Impact assessment of toxic substances. Besides embodying value choices, USES-LCA is also characterised by a number of major modeling uncertainties (Huijbregts et al., 2000b; Ragas et al., 1999)


Besides embodying value choices, USES-LCA is also characterised by a number of major modeling uncertainties (Huijbregts et al., 2000b; Ragas et al., 1999). In this respect its modeling of the fate of metals is particularly weak. In the first place, box models such as USES-LCA include no spatial differentiation of fate, exposure/intake or effect parameters. Although in the case of organic chemicals spatial variability may not be very important in fate and effect assessment compared with the influence of parameter uncertainties (Hertwich et al., 1999), this may not be true of metals. Intermedia transport of metals is highly dependent on environmental conditions, leading to major potential variation in residence time, particularly in the soil compartment (De Vries & Bakker, 1998). For metals, then, adoption of spatially differentiated models (e.g


For metals, then, adoption of spatially differentiated models (e.g. Klepper & Den Hollander, 1999; Van den Hout et al., 1999; Stolwijk et al., 1998) will lead to improved assessment of fate, exposure/intake and ultimate effects. Such models would also permit inclusion of site-dependent processes that are currently lacking in USES-LCA, such as slow conversion of reversibly adsorbed heavy metals into forms irreversibly adsorbed to the soil matrix (Harmsen, 1992; De Vries & Bakker, 1998) and uptake by organisms (Peijnenburg et al., 1997, 1999). Further research in the LCA context is recommended here. The second aspect relates to the fate of geochemically reactive metals such as beryllium in the marine environment. Goldberg (1965) reports an oceanic residence time of Be three orders of magnitude lower than calculated by USES-LCA (Huijbregts, 2000)


Goldberg (1965) reports an oceanic residence time of Be three orders of magnitude lower than calculated by USES-LCA (Huijbregts, 2000). This is because Be-ions are anticipated to be rapidly hydrolysed by the pH of seawater and incorporated into minerals such as ferro-mangenese nodules (Goldberg, 1965; Riley, 1971). This removal mechanism is not included in USES-LCA. For other metals such as copper, zinc, lead, cadmium and vanadium, too, minor uncertainties in burial processes may lead to major uncertainties in steady-state concentrations, and hence, toxicity potentials. The third aspect concerns the topic of the so-called essential elements


The third aspect concerns the topic of the so-called essential elements. According to Alloway (1990) there are three criteria to determine whether or not a chemical element is biologically essential: the organism can neither grow nor complete its life cycle without an adequate supply of the element; the element cannot be wholly replaced by any other element; the element has a direct influence on the organism and is involved in its metabolism. Cobalt, copper, chromium, manganese, selenium and zinc are examples of essential elements (in this case heavy metals) which are indispensable for life and may be deficient in some situations with associated problems for plants, animals and even human beings; addition of such essential elements to the environment may then have a positive effect


On the other hand, uptake of the same essential heavy metals above a certain level may have toxic impacts on plants, animals and human beings; addition of such essential elements to the environment may then have negative consequences. A discussion on how to deal with the possible positive and negative effects of essential heavy metals in the derivation of effect factors for these elements has been going on now for quite a while, but has not yet led to new derivation procedures. The derivation procedures described in this Guide for effect factors for humans, terrestrial and aquatic ecosystems give no consideration to this ongoing discussion. It is indeed to be queried if this issue is resolvable at all.


Part 3: Scientific background Concluding, while the method of Huijbregts (1999a) models fate and exposure routes more realistically and comprehensively than that of Hauschild & Wenzel (1998), thereby using continuous variables, there are still major uncertainties in the underlying model and its constituent parameters. Particular care should be taken if results are likely to hinge prominently on heavy metals, which score high in this method because of their persistency (this should be checked as part of the contribution analysis; see Section 5.4), This is particularly true in the case of Be and Cr. The method has, in principle, been operationalised for some 180 substances (Huijbregts, 1999a). Hertwich (1999) has also recently calculated human toxicity potentials, employing the multimedia model CalTOX in a similar approach to Guinée et al. (1996) and Huijbregts (1999a; see also Huijbregts et al., 2000a)


(1996) and Huijbregts (1999a; see also Huijbregts et al., 2000a). These so-called Toxicity Equivalence Potentials (TEPs) have been developed for air and water emissions for approximately 280 substances. The following appear to be the main differences between TEPs and the HTPs of Huijbregts (1999a; see also Huijbregts et al., 2000a and www.scorecard.org/envreleases/def/tep_qen.html ): CalTOX considers more exposure routes than USES-LCA; the effect part of human toxicity assessment is based not on ADIs but on other human risk factors; CalTOX is based on American environmental data, USES-LCA on European data; TEPs have been developed for emissions to air and water only, HTPs for emissions to air, freshwater, seawater and (‘agricultural’ and ‘industrial’) soil; TEPs are available for about 280 substances, HTPs for about 180 substances


A more thorough comparison of the human toxicity factors developed by Huijbregts (1999a; see also Huijbregts et al., 2000a) and Hertwich (1999) was beyond the scope of the present study. Hofstetter (1998) also used a multimedia model for assessing human toxicity, but now as part of an endpoint approach (cf. Section 4.2 for a more general description of the endpoint, or damage approach). This method, which has been developed as part of the Eco-indicator 99 approach (Goedkoop & Spriensma, 1999), has been operationalised for a limited number of substances only. Fate analysis is based on the USES 1.0 model (RIVM et al., 1994), in much the same way as in Guinée et al. (1996). Several improvements have been introduced, however, including modeling of substance-specific dilution height. The resultant concentrations in the various environmental compartments and the ensuing, aggregated Predicted Daily Intake are then used to calculate fate factors


The resultant concentrations in the various environmental compartments and the ensuing, aggregated Predicted Daily Intake are then used to calculate fate factors. The damage to human health resulting from exposure to the selected substances, estimated from a range of studies, experiments and epidemiological data, is expressed in terms of Disability Adjusted Life Years (DALYs). The concept of DALYs is described in detail in Hofstetter (1998) and briefly reviewed above in the text box in Section 4.2. The fate factors and DALYs are used to calculate characterisation factors representing the damage to human health per kg substance emitted to air or water, expressed in DALYs. 3. Methods based on models and empirical relations Jolliet & Crettaz (1997) have developed characterisation factors for human toxicity based on singlemedium models and empirical measurement data to account for degradation and intermedia transport. The methods of Guinée et al


The methods of Guinée et al. and Huijbregts focus more on intermedia transport modeling, that of Jolliet & Crettaz on empirical exposure relation based on measurement data or single-medium models, the main two differences being that the latter takes account of inter-substance variation in dilution volume in the final compartments air and water, and (for several substances) uses a ratio between emission flow to air and resultant ambient concentration based on empirical data rather than modeling. All other fate routes are simulated using various partial models, however. Moreover, the methods of Guinée et al. (1996) and Huijbregts (1999a) apply the same model for all routes, while that of Jolliet & Crettaz (1997) employs several empirical relations derived from different unrelated sources (measurement data and single-medium models). The latter method is summarised in the following formula:


Part 3 : Scientific background with: HTP Human Toxicity Potential (dimensionless); the effect factor, representing the human-toxic impact of substance i in final compartment fcomp and here defined as the reciprocal of the ‘total acceptable world annual dose’ per for air the NEC (No Effect Concentration in times the total volume of air inhaled by human beings per year per and for water and soil the ADI (in kg·kg body times total body weight per and number of days per year, i.e. 365. It is expressed in different units for each fcomp; the fate factor for substance i, incorporating intermedia transport between emission compartment ecomp and final compartment fcomp and degradation in fcomp (with different units for each fcomp). The method yields a single indicator result for human toxicity


The method yields a single indicator result for human toxicity. For 17 substances has been calculated on the basis of empirical data, generalising the ratio between the measured concentration in Switzerland and the corresponding total emission flow to the world level. For about 100 other substances is based on the ratio between the residence time of the pollutant and the dilution height in the steady state is based on the ratio between the residence time of the pollutant and the dilution depth in the steady state. The ExternE project (EC, 1995a) has also assessed the potential human health impacts of air pollutants, the carcinogenic effects of trace metals, dioxins and radionuclide emissions and industrial accidents affecting members of the public. However, the format in which the dose-response functions have been published precludes derivation of characterisation factors


However, the format in which the dose-response functions have been published precludes derivation of characterisation factors. Thus far, the main focus of discussion has been on the fate (and sometimes exposure/intake) element of human toxicity, leaving aside the actual effects, to which we now turn. Most methods for assessing human toxicological effects are based on the use of certain threshold values, expressed in the ‘effect factor’ as the reciprocal of an ADI or something similar. For such methods, the prevailing background concentration is unimportant. There is, however, a trend towards incorporating epidemiological studies on the effects actually occurring at present background concentration levels, as in the endpoint approaches of, for example, Hofstetter (1998), Goedkoop & Spriensma (1999) and EC (1995a) (cf. Section 4.2). In both endpoint and midpoint approaches, there is the question of aggregation


Section 4.2). In both endpoint and midpoint approaches, there is the question of aggregation. In the case of midpoint approaches, the question is whether every ADI is equally important or whether ADIs for carcinogenic chemicals are more important than those for allergenic chemicals. Most approaches attach equal weight to every threshold value. This simple weighting procedure for human toxicity does not fulfil the ISO criteria for life cycle impact assessment (ISO, 1998a). This limitation can be overcome by breaking down human toxicity into several subcategories. Burke et al. (1996), for instance, has proposed dividing human-toxic substances into three categories, viz. those associated with irreversible effects, with reversible but life-threatening effects, and with reversible and non-life-threatening effects. Expert-based weighting factors of 100, 10 and 1, respectively, have been assigned to these three subcategories


Expert-based weighting factors of 100, 10 and 1, respectively, have been assigned to these three subcategories. Aggregation may also be based on the concept of Disability Adjusted Life Years (DALYs). All the methods developed to date for the impact category human toxicity suffer from a number of fundamental shortcomings. Perhaps the most serious of these is the (value) choice to effectively attach the same weight to all toxicological effects. Two other major simplifications are the assumed linearity between emissions and potential effects (cf. Owens, 1997b) and the complete disregard of chemical, environmental, metabolic en toxicological interactions (incl. synergy) between individual substances. Given the paucity of data currently available, the latter limitations are particularly difficult to overcome (cf. Huijbregts etal., 2000a). PROSPECTS In the future, further progress is anticipated on Impact assessment modeling to the various endpoints, viz


Huijbregts etal., 2000a). PROSPECTS In the future, further progress is anticipated on Impact assessment modeling to the various endpoints, viz. damage to human health, damage to ecosystem health, etc. (cf. Figure 4.2.2); see Section 4.2 for a more extensive discussion of this subject). CONCLUSIONS Despite these fundamental limitations, which are not specific to LCA but are also encountered in RA, we still consider it possible and useful to recommend a baseline method for the impact category human toxicity. It is possible, since there are practical methods available reflecting, as well as is feasible, the 1 In Jolliet & Crettaz this ratio is called which is not consistent with the earlier definition.


Part 3: Scientific background state-of-the-art in human toxicity assessment; and useful, since the alternative would be that potentially human-toxic emissions would otherwise be only qualitatively flagged, clouding final assessment, or even omitted altogether. As the baseline characterisation method for human toxicity we thus recommend using the method of Huijbregts (1999a), based on fate modeling with USES-LCA. USES-LCA treats intermedia transport most realistically and comprehensively, using continuous variables and a multimedia model. In our view this is superior to the ‘rule of thumb’ method developed by Hauschild & Wenzel (1998) and the mix of models and measured data used by Jolliet & Crettaz (1997) (criterion 8 for the selection of baseline characterisation methods). Those of Huijbregts (1999a) and Hertwich (1999) appear to be very similar. Both methods are operational, while the method of Hertwich (1999) includes more substances (criterion 11)


Those of Huijbregts (1999a) and Hertwich (1999) appear to be very similar. Both methods are operational, while the method of Hertwich (1999) includes more substances (criterion 11). However, Huijbregts’ method can also be used for emissions to soil. This is especially important for LCAs on agricultural products, where emissions to soil are significant. Huijbregts’ method encompasses both human toxicological and ecotoxicological effects (see Section 4.3.8), moreover, enabling both impact categories to be assessed using the same method. The method of Huijbregts is thus preferable to that of Hertwich (1999) and in this Guide is therefore recommended as the baseline. An infinite time horizon is thereby taken (criterion 7) and a global spatial scale (criterion 10). It should be borne in mind, however, that the list of 180 substances provided by Huijbregts (1999a) represents merely a very small subset of all known and unknown toxic substances


It should be borne in mind, however, that the list of 180 substances provided by Huijbregts (1999a) represents merely a very small subset of all known and unknown toxic substances. If the case study involves substances suspected of contributing to human toxicity for which HTPs are not provided in this Guide, such factors should be calculated. If this is not feasible, an attempt should be made to estimate HTPs for these substances based on similar or related substances for which HTPs are available (by extrapolation, for example; cf. Section 4.3.17). It is not recommended to use the ‘old’ characterisation factors that ignore fate, such as those developed by Heijungs et al. (1992), even if these are available for more substances than the new, fate-based factors. Fate is a particularly important consideration in the context of the human toxicity of chemical pollutants and its exclusion might lead to misleading results


Fate is a particularly important consideration in the context of the human toxicity of chemical pollutants and its exclusion might lead to misleading results. The baseline characterisation method for human toxicity, using the characterisation factor is evaluated with respect to the (ISO-based) criteria in Table 4.3.7.1. Table 4.3.7.1: Evaluation of the baseline characterisation method for human toxicity, using the characterisation factor with respect to the (ISO-based) criteria of Table 4.3.1. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12


criterion scientifically and technically valid environmentally relevant internationally accepted value-choices and assumptions focal point in environmental mechanism linearity time span fate, exposure/intake and effects less is better time- and location-independent operational uncertainty margins evaluation partly: although fate and exposure/intake calculations are based on well understood environmental mechanisms, actual toxicological effects are assessed via a very crude aggregation of very different effects (from skin irritation to mortality) yes, the category indicator represents risks to humans (nearendpoint level) no; however, USES-LCA is very similar to the model EUSES, supported by the European Union (EUSES is in fact based on USES) yes; the choice of valuing all effects equally is particularly debatable (nearly) endpoint yes eternity fate, exposure/intake and effects included yes; below-threshold effects are also included yes, although in fact the HTPs are European factors


is particularly debatable (nearly) endpoint yes eternity fate, exposure/intake and effects included yes; below-threshold effects are also included yes, although in fact the HTPs are European factors yes, for 180 substances emitted to air, water and soil presumably about the same as for the other category indicators that include fate, discussed above The following methods are included in this Guide as options for sensitivity analysis, particularly for LCA studies in which results for this impact category are dominated by metals and volatile, persistent halogenated organics: The HTPs for the time horizons 20, 100 and 500 years at the global scale: Huijbregts et al


(2000). The HTPs for an infinite time horizon at the continental scale (i.e. excluding global effects): Huijbregts et al. (2000).


Part 3 : Scientific background Recommendations for extended LCAs: If the case study involves substances suspected of contributing to human toxicity for which HTPs are not provided in this Guide, such factors should be calculated. If this is not feasible, an attempt should be made to estimate HTPs for these substances based on similar or related substances for which HTPs are available (by extrapolation, for example; cf. Section 4.3.17)


Section 4.3.17). method status baseline alternative 1 alternative 2 alternative 3 alternative 4 additional variant characterisation method/factor reference Huijbregts, 1999a Huijbregts et al., 2000 Huijbregts et al., 2000 Huijbregts et al., 2000 Huijbregts et al., 2000 Hauschild & Wenzel, 1998; Guinée et al., 1996; Hertwich, 1999; Jolliet & Crettaz, 1997 RESEARCH RECOMMENDATIONS Short-term research: It is recommended to undertake a detailed comparison of the factors of Huijbregts (1999a) and Hertwich (1999) to establish which differences are due to differences in data and which to the use of different fate and effect models. It is recommended to develop characterisation factors for group parameters according to the procedures described in Section 3.6. It is recommended to establish a ‘helpdesk’ to provide support for calculation of characterisation factors for substances for which these factors are currently lacking


It is recommended to establish a ‘helpdesk’ to provide support for calculation of characterisation factors for substances for which these factors are currently lacking. Long-term research: Further research to explore the potential for using Disability Adjusted Life Years (DALYs) in LCA is recommended, especially for this impact category. The possible integration of toxic effects on human health with other human health effects such as the effects of photo-oxidants, radiation, casualties, etc., should also be investigated. The DALY concept might be very suitable for the purpose of such integration. The EDIP approach, employing a database of impacts in the working environment per kilogram of goods produced for specific economic activities may represent a valuable addition to LCA (Schmidt & Brunn Rasmussen, 1999). This database comprises mainly Danish data, however, and research should be undertaken to establish a similar database with European data


This database comprises mainly Danish data, however, and research should be undertaken to establish a similar database with European data. 4.3.8 Ecotoxicity TOPIC This impact category covers the impacts of toxic substances on aquatic, terrestrial and sediment ecosystems. The area of protection is the natural environment (and natural resources) (see Figure 4.2.2). For this impact category a variety of characterisation methods are available, providing characterisation factors generally that are referred to as ‘ecotoxicity potentials’ (ETPs). As in the previous section, on human toxicity, the following general formula will here be used as a point of departure for describing and discussing these different methods:


Part 3: Scientific background with: the ecotoxicity potential: the contribution to ecotoxicity of a unit emission of substance i, to emission compartment ecomp. Most methods distinguish several subcategories, such as AETP for aquatic ecotoxicity, TETP for terrestrial ecotoxicity, etc.; a ’fate factor’, representing intermedia transport of substance i from emission compartment ecomp to final (sub)compartment fcomp, and degradation within compartment ecomp; in some methods intermedia transport is indicated separately by and (bio)degradation by an ’effect factor’, representing the toxic effect of exposure of a given ecosystem to substance i in compartment fcomp. The ETP is often defined relative to a reference substance. As a formula: with the symbols similar to the above. The choice of the reference substance is arbitrary


The ETP is often defined relative to a reference substance. As a formula: with the symbols similar to the above. The choice of the reference substance is arbitrary. DEVELOPMENTS IN THE LAST DECADE Ecotoxicological impact, together with human toxicological impact, is the impact category for which fate and particularly intermedia transport are most important. Toxic substances do not generally remain in the environmental compartment into which they are emitted, but tend to spread to other compartments, where they may do more damage. Airborne insecticides will also settle out in waterways, for example, where they may cause (severe) harm to aquatic organisms. As with human-toxic impacts, many authors therefore stress that it is essential to properly incorporate intermedia transport in LCA models of ecotoxicological impact (e.g. Heijungs et al., 1992; Lindfors et al., 1995c; Jolliet, 1996; Guinée et al., 1996; Jolliet & Crettaz, 1997; Hauschild & Wenzel, 1998). Heijunas et al


Heijungs et al., 1992; Lindfors et al., 1995c; Jolliet, 1996; Guinée et al., 1996; Jolliet & Crettaz, 1997; Hauschild & Wenzel, 1998). Heijunas et al. (1992) In Heijungs et al. (1992) this impact category covered only toxic emissions to the environmental compartments water and soil. Emissions to water were considered to be toxic to aquatic ecosystems only and emissions to soil toxic to terrestrial ecosystems only. Separate characterisation factors were thus defined for emissions to water and soil: substance and kg substance) and and are the Maximum Tolerable Concentrations for water and soil, derived according to a method developed by EPA and modified by RIVM (EPA, 1984; Van de Meent et al., 1990; Slooff, 1992). These MTCs represent the concentration considered to protect 95% of the species in an ecosystem


These MTCs represent the concentration considered to protect 95% of the species in an ecosystem. They are based on ecotoxicological data on the species sensitivity tochemical substances, usingthe modified EPAmethod, which employs safetyfactors basedonthe numberof test species included (see, inter alia, RIVM et al., 1994). The characterisation factors used to assess and aggregate the interventions for the impact categories aquatic and terrestrial ecotoxicity are: where (mg) is the amount of substance i emitted to water and the amount of substance i emitted to soil. The indicator results terrestrial ecotoxicity and aquatic ecotoxicity are expressed in kg soil and water. They can be interpreted as the quantity of terrestrial or aquatic ecosystem polluted to the MTC. As with the impact category“human toxicity”, Heijungs et al. again stress that these characterisation factorsforecotoxicityshould be considered as no more than indicative until such time as a better method is developed


again stress that these characterisation factorsforecotoxicityshould be considered as no more than indicative until such time as a better method is developed. where ECA and ECT are, respectively, the characterisation factor for aquatic and for terrestrial ecosystems


Part 3 : Scientific background In line with these recommendations, we here consider only methods that include degradation and intermedia transport, which supersede the provisional method developed by Heijungs et al. (1992)1. (See text box.) Several such methods are described in the literature, falling roughly into three groups: The architects of the intermedia transport-based characterisation factors for human toxicity described in the previous section have developed such factors for ecotoxicity, with methods once more falling roughly into three groups: methods in which degradation and intermedia transport is based on simple rules of thumb (e.g. Hauschild & Wenzel, 1998) methods in which degradation and intermedia transport is based on models (e.g. Guinée et al., 1996; Huijbregts, 1999a) methods in which degradation and intermedia transport is based on models and empirical relations (e.g. Jolliet & Crettaz (1997). 1. 2. 3


Guinée et al., 1996; Huijbregts, 1999a) methods in which degradation and intermedia transport is based on models and empirical relations (e.g. Jolliet & Crettaz (1997). 1. 2. 3. In this section we consider only those aspects of these methods and models of specific relevance for the impact category ‘ecotoxicity’, referring the reader for background to Section 4.3.7 on ‘human toxicicity’, as necessary. As in the previous section, these methods are discussed below in terms of a common notation. For instance, F always denotes a fate factor, although a variety of symbols and terms are encountered in the literature. Moreover, the precise operationalisation and , consequently, the units used for broadly similar variables may differ from method to method. 1


Moreover, the precise operationalisation and , consequently, the units used for broadly similar variables may differ from method to method. 1. Methods based on rules of thumb Based on rule of thumb, Hauschild & Wenzel (1998) have developed characterisation factors2 incorporating intermedia transport for three3 different impact categories: with: the Aquatic EcoToxicity Potential of substance i emitted to emission compartment ecomp, with an indication of the type of impact considered (acute or chronic the Terrestrial EcoToxicity Potential of substance i emitted to emission compartment ecomp, with an indication of the type of impact considered (in this case only chronic the intermedia transport factor, the fraction of substance i emitted to emission compartment ecomp that reaches final compartment fcomp as a result of environmental transport


The factor is based on a simple rules of thumb rather than on a full fate model; moreover, it is not a continuous variable but assumes only a limited number of values such as 0.2 and 1; the biodegradability factor, to be chosen from one of three classes depending on the substance i involved; the effect factor, representing the (acute and/or chronic) toxic impact of substance i on aquatic or terrestrial ecosystems (respectively) in final compartment fcomp; depending on fcomp and the type of effect, it is the reciprocal of a PNEC4 (Predicted No Effect Concentration). Three different emission compartments are considered by Hauschild & Wenzel: air, water and soil. The effect compartments considered are water and soil. In the aquatic compartment acute and chronic 1 Heijungs et al. stressed in 1992, already, that fate should be included; at the time, however, this could be done only provisionally


In the aquatic compartment acute and chronic 1 Heijungs et al. stressed in 1992, already, that fate should be included; at the time, however, this could be done only provisionally. 2 Again, for reasons of terminological consistency we here employ a number of terms as originally defined by Hauschild & Wenzel. 3 Acute toxicity to (the micro-organisms in) wastewater treatment plants has been suggested as a fourth impact category, but waste water treatment plants are considered to be part of the economic system in this Guide (cf. Section 3.2), we have restricted the discussion to the three environmental impact categories acute aquatic ecotoxicity, chronic aquatic ecotoxicity and chronic terrestrial ecotoxicity. 4 The PNEC is based on species-specific ecotoxicological data derived by a variety of methods, in some cases policy targets (see Hauschild & Wenzel, 1998). ); );


Part 3: Scientific background effects are considered separately. Together this results in seven different ecotoxicity potentials per substance. Hauschild & Wenzel’s method yields three separate indicator results for ecotoxicity: one for chronic effects in the aquatic compartment: one for acute effects in the aquatic compartment: and one for chronic effects in soil compartment: AETP and TETP are defined as specified above, and is the emission of substance i to compartment ecomp. These indicator results are not aggregated to a single indicator result. Overall, it can be concluded that the method of Hauschild and Wenzel (1998) models degradation and intermedia transport only very rudimentarily. 2. Model-based methods Guinée et al. (1996) have also developed characterisation factors for aquatic and terrestrial ecotoxicity impacts that take some account of degradation and intermedia transport


Model-based methods Guinée et al. (1996) have also developed characterisation factors for aquatic and terrestrial ecotoxicity impacts that take some account of degradation and intermedia transport. The method they employed is basically the same as that used for human toxicity impacts and is based once more on the USES 1.0 model, which includes Simplebox 1.0 as a model. A Predicted Environmental Concentration (PEC)1 is calculated for the compartments water, agricultural soil and industrial soil resulting resulting from a given constant emission to air, water, agricultural or industrial soil (i.e. soil used for agricultural and industrial purposes). The PEC in water is divided by the Predicted No Effect Concentration2 (PNEC) for aquatic ecosystems, the PEC in soil by the PNEC for terrestrial ecosystems


The PEC in water is divided by the Predicted No Effect Concentration2 (PNEC) for aquatic ecosystems, the PEC in soil by the PNEC for terrestrial ecosystems. Finally, the score is divided by the score for the emission of a reference substance, resulting in: with: the Aquatic EcoToxicity Potential due to emission of 1000 kg of substance i per day (flux) to initial emission compartment ecomp (dimensionless); the Terrestrial EcoToxicity Potential, defined in a parallel manner (dimensionless); the predicted concentration of substance i in water due to the emission to compartment ecomp; is the same predicted concentration but for 1,4-dichlorobenzene the predicted concentration of substance i in agricultural soil due the emission to compartment ecomp; PECagricultural is the same predicted concentration but for 1,4-dichlorobenzene (dimensionless)


the effect factor representing the toxic impact of substance i on aquatic ecosystems, here the reciprocal of the PNEC for terrestrial ecosystems; is the same effect factor but for 1,4-dichlorobenzene is an alternative formulation of 2 In this case the PNEC was defined similarly to the MTC: the concentration held to protect 95% of the species in an ecosystem. This PNEC was also based on species-specific ecotoxicological data using a variety of methods; see VROM (1997).


Part 3 : Scientific background the effect factor representing the toxic impact of substance i on terrestrial ecosystems, here the reciprocal of the PNEC for terrestrial ecosystems; is the same effect factor but for 1,4-dichlorobenzene (dimensionless). The compartments considered are air, water and agricultural and industrial soil, resulting in four AETPs and four TETPs per substance. The result is one indicator result for aquatic ecotoxicity and one for terrestrialecotoxicity: where and are as defined above and is the amount of substance i emitted to compartment ecomp (kg)


The result is one indicator result for aquatic ecotoxicity and one for terrestrialecotoxicity: where and are as defined above and is the amount of substance i emitted to compartment ecomp (kg). with: the Freshwater Aquatic EcoToxicity Potential of substance i emitted to emission compartment ecomp; This is extended to the other impact categories, giving the following scheme: impact (sub)category freshwater ecotoxicity freshwater sediment ecotoxicity marine aquatic ecotoxicity marine sediment ecotoxicity terrestrial ecotoxicity characterisation factor These ecotoxicity potentials are based on a PEC/PNEC ratio, weighted as necessary on the basis of the volume (water) or weight (soil and sediment) of the compartments/scales concerned (see also Section 4.3.7 and, for a more detailed explanation, Huijbregts, 1999a). For these ecotoxicity potentials, Huijbregts et al. (2000) again used scenario analysis to assess the sensitivity of the model to different time and spatial horizons


For these ecotoxicity potentials, Huijbregts et al. (2000) again used scenario analysis to assess the sensitivity of the model to different time and spatial horizons. For a discussion of this topic we refer the reader to Section 4.3.7, for a detailed description of calculation procedures and models used to Huijbregts (2000). As in the case of human toxicity, the method of Huijbregts (1999a) takes degradation and intermedia transport routes into account more realistically than that of Hauschild & Wenzel (1998). The method is operational for 181 substances, in principle. Huijbregts (1999a, 2000) developed the USES-LCA model to calculate not only new characterisation factors for human toxicity but also for five subcategories of ecotoxicity: freshwater aquatic, marine aquatic, freshwater sediment, marine sediment and terrestrial, each of them for different time horizons. The main differences from Guinée et al. have already been described (Section 4.3.7)


The main differences from Guinée et al. have already been described (Section 4.3.7). The factors proposed by Huijbregts are:


Part 3: Scientific background 3. Methods based on models and empirical relations Jolliet & Crettaz (1997) also developed their ‘critical surface time’ method for ecotoxicity, calculating characterisation factors for aquatic and terrestrial ecotoxicity: with: the effect factor representing the toxic impact of substance i on aquatic ecosystems and here defined as the reciprocal of the PNEC1 (Predicted No Effect Concentration for aquatic ecosystems); the effect factor representing the toxic impact of substance i on terrestrial ecosystems and here defined as the reciprocal of the PNEC1 (Predicted No Effect Concentration for terrestrial ecosystems); the fate factor for water, the fraction of substance i emitted to emission compartment ecomp that reaches the final compartment water, the fate factor for soil, the fraction of substance i emitted to emission compartment ecomp that reaches the final compartment soil; and are as above


These fate facors are based on the same intermedia transfer factors as for human toxicity. Thus far, the main focus of discussion has been on the fate element of ecotoxicity. We now turn to the actual effects. Most methods for assessing ecotoxicological effects are based on the use of certain threshold values, expressed in the ‘effect factor; as the reciprocal of a PNEC os something similar. For such methods, the prevailing background concentration is unimportant. There is, however, a trend towards incorporating concentration-effect curves, or the slopes of such curves, in which the effect factor is dependent upon the background concentration. This is the case, for example, in the endpoint approach used in the Eco-indicator 99 (Goedkoop & Spriensma, 1999; cf. Section 4.2)


This is the case, for example, in the endpoint approach used in the Eco-indicator 99 (Goedkoop & Spriensma, 1999; cf. Section 4.2). This is based on the notion of PAFs (Potentially Affected Fraction), the fraction of species that, given an environmental concentration, is exposed above the No Observed Effect Concentration (NOEC) (Van de Meent, 1999). The higher the concentration, the greater the percentage of species considered to be affected. PAFs are based on substance-specific species-sensitivity distributions, in turn are based on the NOECs for these substances for different species. The PAFconcentration curve typically has a log-log shape (see Figure 4.3.8.1). 1The PNECs of Jolliet & Crettaz (1997) are based on species-specific ecotoxicological data, similarly to the MTCs of Heijungs et al. 1992; in this case the modified EPA method was used (see, inter alia, RIVM et al., 1994).


Part 3 : Scientific background The PAF and the PNEC are both based on the same curve (see Figure 4.3.8.1). To apply the PAF concept requires additional data, however, for example on current environmental concentrations. As these data are still lacking for most substances, the concept still has little practical use. The fact that the PAF provides a measure of toxicity that is comparable between substances is considered to be its major advantage. However, specific rules of addition hold for combining PAFs, which are difficult to transpose to LCA, because as a baseline LCA integrates over space and time and these addition rules for combined toxicity are of course only valid when the substances are present at the same place at the same time. While ecotoxicological impact assessment is based on PAFs in the Eco-indicator 99 method, fate and exposure modeling is based on EUSES, a European expansion and update of USES 1.0


While ecotoxicological impact assessment is based on PAFs in the Eco-indicator 99 method, fate and exposure modeling is based on EUSES, a European expansion and update of USES 1.0. The method is operational for 46 substances emitted to air, water and soil. The methods used to derive characterisation factors for ecotoxicological impacts very much resemble those employed for human toxicological impacts. There is one major difference, however. In the case of human toxicity the ADI is often taken as a reference level for risk evaluation. The ADI (Acceptable Daily Intake) represents an acceptable risk level for one species: humans, based on extrapolation of a range of known human toxicological effects. In the case of ecotoxicity a PNEC (Predicted No Effect Concentration) or MTC (Maximum Tolerable Concentration) is used as a reference level to evaluate the risk


In the case of ecotoxicity a PNEC (Predicted No Effect Concentration) or MTC (Maximum Tolerable Concentration) is used as a reference level to evaluate the risk. These levels represent an acceptable risk level for ecosystems and are based on extrapolation of selected toxic effects on a few selected species to the overall ‘toxic impact’ on an entire ecosystem. PROSPECTS In the future, further progress is anticipated on Impact assessment modeling to the various endpoints, viz. damage to human health, damage to ecosystem health, etc. (cf. Figure 4.2.2); see Section 4.2 for a more extensive discussion of this subject). In this respect the PAF method seems promising, but requires further work and operationalisation. At the moment the EPA is working on a list of so-called ‘PBT’ chemicals that are persistent, bioaccumulative and toxic, to be used to focus source-reduction and recycling measures