31.2.2 Chemical Synthesis and Production Processes LCA is a useful tool for improving existing processes and designing new processes for the synthesis and production (Fig. 31.3, stages A–D) as well as for the end-of-life treatment (Fig. 31.3, stage F) of chemicals and chemical products, to inform process systems engineering decisions (Jacquemin et al. 2012). In this section, LCA case studies focusing on chemical synthesis and production processes across various economic sectors are discussed. P. Fantke and A. Ernstoff


A major issue illustrated by several LCA studies is that management decisions based on single indicators or criteria can lead to increasing other impacts (that were not considered in the decisions), thereby indicating the strength of LCA as an approach to assess multiple indicators and related trade-offs. An example is the development and application of new plant protection products (pesticides) designed with the intention to reduce human toxicity and ecotoxicity potentials associated with emissions after application in agricultural crop protection or elsewhere (e.g. household pesticides). A related LCA study revealed that the production of a new and more effective plant growth regulating pesticide with less intrinsic toxicity (preferable from a risk perspective) than a functionally equivalent earlier marketed pesticide comes at the expense of increased impacts associated with pesticide synthesis and production processes (Geisler et al. 2005)

31 LCA of Chemicals and Chemical Products

Hauschild et al., LCA: Theory and Practice


2005). The higher impacts for the new pesticide are mostly explained by the high complexity of its molecular structure requiring more synthesis and processing steps. In general, impacts related to the production of chemicals have been attributed to energy consumption which tends to increase with increasing complexity of a chemical molecule. Highly specialised chemicals, such as pharmaceuticals, can thereby be associated with higher energy consumption and related impacts from synthesis and production processes than other chemicals (Wernet et al. 2010). Not only complexity of chemical synthesis and production processes, but also the difference in raw materials used drives environmental performance profiles of chemicals and chemical products. This is shown in another set of LCA studies contrasting chemical production from fossil fuel-based versus renewable (bio-based) resources. Synthesising and producing chemicals from biomass (e.g. sugar cane) instead from fossil fuels (e.g


Synthesising and producing chemicals from biomass (e.g. sugar cane) instead from fossil fuels (e.g. petroleum or natural gas) has been proposed as a ‘sustainable technology’ with respect to reducing reliance on fossil resources and greenhouse gas emissions. However, a full sustainability analysis has typically not been conducted, which is why several LCA studies have focused on this claim. As an example, a simplified overview of the different chemical synthesis and processing steps involved in polyethylene terephthalate (PET) polymer production is given in Fig. 31.5. While terephthalic acid used in the production of the chemically identical PET and bio-PET is in both cases derived from petroleum, ethylene glycol can be derived from natural gas as a fossil resource (for PET) or from sugar cane as a biomass feedstock (for bio-PET)


The process of natural gas refinement to create ethylene glycol alone consists of several steps including cracking (breaking down) into ethylene and other chemicals, ethylene separation and purification involving several distillation processes (not shown in Fig. 31.5). Accordingly, LCA studies have found that bio-based chemical production usually can lead to less greenhouse gas emissions than fossil-based chemical production, mainly because less refinement of fossil fuels is required. However, growing, harvesting and processing bio-based feedstocks may lead to other impacts related to agriculture production systems, e.g. land use (see Chaps. 29 and 30), which are highly variable with respect to the type of biomass used (Tabone et al. 2010; Akanuma et al. 2014). Furthermore, the type of biomass used can influence the energy required, and LCA of Chemicals and Chemical Products


post-processing of bio-based products and residues greatly influence the overall related environmental performance. Other LCA studies have focused on specific aspects of chemical synthesis and processing, such as comparing continuous and batch reactor types (e.g. Wang et al. 2013) or different catalysis and fermentation processes (e.g. Pati et al. 2014). It is further important to consider which catalysts are used in other processing steps that petro- and bio-based materials like PET have in common, such as antimony trioxide found at concentrations of 200–300 ppm in PET or other, metal-free catalysts used in the polycondensation process as part of polymerisation. Several studies have concluded that processes with higher yields have a lower impact per chemical production unit. The use of solvents has additionally been identified as an important component influencing environmental performance of chemical products (De Soete et al. 2014)


The use of solvents has additionally been identified as an important component influencing environmental performance of chemical products (De Soete et al. 2014). Generally, and specifically for chemical synthesis and processing, it is important to be sceptical of processes and products labelled or deemed ‘green’ or ‘sustainable’ without performing a full LCA as shown, e.g. for ‘green’ nano-materials synthesis (Pati et al. 2014). An overview of aspects that are relevant for assessing ‘green’ chemical synthesis and production processes is given by Kralisch et al. (2015)


for ‘green’ nano-materials synthesis (Pati et al. 2014). An overview of aspects that are relevant for assessing ‘green’ chemical synthesis and production processes is given by Kralisch et al. (2015). PET bio-PET Petroleum refinery xylene terephthalic acid ethylene (petro) ethylene oxide ethylene glycol bio-ethylene bio-ethylene oxide bio-ethylene glycol bio-ethanol Natural gas refinery glucose molasses (by-product) Livestock feed Energy bagasse (leŌover biomass) 2 CO2 H2O O2 H2O O2 O2 H2O fermentaƟon dehydraƟon oxidaƟon oxidaƟon catalyƟc oxidaƟon oxidaƟon oxidaƟon combusƟon polymerizaƟon polymerizaƟon Sugar cane harvest desugarizaƟon ~70% ~30% steam cracking fluid catalyƟc cracking Fig. 31.5 Production process steps for chemical synthesis of polyethylene terephthalate (PET) derived from fossil fuels and bio-PET (partly) derived from bio-resources (modified from Tabone et al. 2010) P. Fantke and A. Ernstoff


31.3 Specific and General Methodological Issues for LCA of Chemicals Applying LCA, specifically in the chemical and pharmaceutical industry, and in other sectors where chemicals play a central role, comes with several methodological and practical challenges. Generally, gathering chemical inventory data, quantifying impacts, and interpreting results constitute challenges for LCA studies across sectors. In the following sections, some of the most relevant challenges focusing on chemicals in LCA are discussed in relation to the definition of the goal and scope of an LCA study, product system modelling and quantification of life cycle chemical emissions in the inventory analysis, characterisation modelling in the impact assessment, and finally interpretation of LCA results in different contexts. 31.3.1 Goal and Scope Definition Consistently defining the goal and scope for chemical products or processes (e.g


31.3.1 Goal and Scope Definition Consistently defining the goal and scope for chemical products or processes (e.g. functional unit of the considered product or service system and related reference flow(s) and system boundaries) is not trivial and needs to be critically considered by a practitioner. Examples of relevant issues when defining functional unit, reference flow(s), and system boundaries are discussed in the following. Functional Unit (FU) LCA (and other types of assessments) can be designed to compare functionally equivalent chemicals and chemical products as classified by chemical function (e.g. solvents, catalysts), material function (e.g. nanotubes, polymers) or product function (e.g. herbicides). It is hence important to define the level of ‘functionality’ based on which a study will be conducted. This functionality must be captured in the definition of the FU of an LCA study as basis for comparing products or systems


This functionality must be captured in the definition of the FU of an LCA study as basis for comparing products or systems. Performing an LCA study is useful for providing valuable insight into which alternative, functionally equivalent chemicals or products provides the function with the lowest overall environmental impact profile, thereby focusing on avoiding burden shifting between different types of environmental impacts. To screen multiple alternatives to harmful chemicals in a particular product application, in contrast, the focus often is not mainly on environmental performance, but on a combination of regulatory compliance, economic and technical feasibility, along with considering hazard and human, environmental and social impacts. In such cases, a chemical alternatives assessment (CAA) might be the preferred approach to identify the most viable solution(s)


In such cases, a chemical alternatives assessment (CAA) might be the preferred approach to identify the most viable solution(s). Chemicals and chemical products can also fulfil more than a single function and, hence, a partial definition of the functional unit could lead to inconsistent LCA of Chemicals and Chemical Products


comparisons if the appropriate product systems are not considered as demonstrated in Example 31.1. Example 31.1 Functional Unit (FU) Take a cosmetic product like shampoo, where different chemical ingredients provide different functions as part of the final shampoo product, e.g. to provide clean, shiny and fragrant hair for one person over 24 h. If the FU is defined with respect to a single shampoo product (one-product system) that cleans the hair of one person (by containing detergents) and makes it shiny (by containing siloxanes) for 24 h, a functionally equivalent service could be also provided by applying two distinct products (two-product system), one being a shampoo that only cleans hair (and does not make it shiny) and another being a conditioner that makes the hair shiny (and does not clean). However, both the one-product and two-product systems should not provide fragrance in order to be consistently compared via the same FU (bold text above) that excludes fragrance


However, both the one-product and two-product systems should not provide fragrance in order to be consistently compared via the same FU (bold text above) that excludes fragrance. Likewise, if the FU is defined to just clean hair for one person over 24 h, comparing LCA results of a shampoo that only provides clean hair to a shampoo that provides clean, shiny and fragrant hair could yield the misleading outcome that the former shampoo ‘performs better’, because the production and related impacts of additional chemicals of the latter shampoo (containing siloxanes for making the hair shiny and terpenes for making the hair fragrant) are related to functions not fulfilled by the shampoo that only cleans hair. Hence, the comparison would be biased by comparing products fulfilling distinct functions. Defining an appropriate FU for multi-functionality (see Chap. 8) is also important


Hence, the comparison would be biased by comparing products fulfilling distinct functions. Defining an appropriate FU for multi-functionality (see Chap. 8) is also important. For example, water and propylene glycol are both effective chemical solvents and, thus, both would fulfil an FU defined with respect to providing the function of a solvent in, e.g. a shampoo product. Propylene glycol, however, provides other functions that water does not provide (e.g. stabiliser, humectant, emulsifier). Therefore, a comparison of propylene glycol and water in an LCA study based on a solvent-based FU would not capture the multi-functionality of propylene glycol. Defining the FU with respect to all functionalities and then providing system expansion when necessary (e.g. water plus a stabiliser plus a humectant plus an emulsifier is functionally equivalent to propylene glycol in shampoo) can be an important consideration in any LCA on chemicals or product systems


water plus a stabiliser plus a humectant plus an emulsifier is functionally equivalent to propylene glycol in shampoo) can be an important consideration in any LCA on chemicals or product systems. It is, hence, important to ensure the product(s) or chemical(s) investigated in an LCA study are functionally equivalent and the FU captures this equivalency appropriately. Reference Flow The reference flows (Chap. 8) in an LCA study reflect the overall amount of goods and/or services that are required to fulfil the defined FU. Taking a no-wash (dry) shampoo versus a conventional (liquid) shampoo as examples, the reference P. Fantke and A. Ernstoff


flows to fulfil an FU of cleaning the hair of one person for one day could be 10 g of the liquid shampoo product plus the (hot) water used to wash the hair. The reference flow for the dry, leave-in, no-wash shampoo could be simply 5 g of powdered product (with no wash-water needed). Furthermore, if functionally equivalent products or chemicals provide different efficiencies to fulfil a defined FU, the different efficiencies need to be accounted for in the reference flow. This issue also points to a problem for cradle to gate LCA studies on chemicals, where it is possible that a chemical could have greater cradle to gate impacts than another chemical per unit mass emitted, but far less of the former chemical is required to fulfil the same FU. Here, pesticides with different efficiencies towards the same pest offer a typical example. System Boundaries The system boundaries (Chap


Here, pesticides with different efficiencies towards the same pest offer a typical example. System Boundaries The system boundaries (Chap. 8) of any defined chemical product or service systems in an LCA study need to capture all relevant processes for the systems being compared. For example, if the purpose of an LCA study is to compare bio- with fossil-based chemical synthesis, the system boundaries must include and differentiate all raw material acquisition processes, namely all refining processes for the fossil-based chemical and the crop production and processing steps for the bio-based chemical (see also Fig. 31.5). However, for these systems, it may not be relevant to include chemical use and disposal stages in the study, whenever these life cycle stages are equivalent in both cases. Such systems are referred to as ‘cradle to (factory) gate’ systems and are common in LCA studies on chemical synthesis and other chemical production processes (Jimenez-Gonzalez and Overcash 2014)


Such systems are referred to as ‘cradle to (factory) gate’ systems and are common in LCA studies on chemical synthesis and other chemical production processes (Jimenez-Gonzalez and Overcash 2014). In contrast, if the purpose of the study is to compare two distinct fossil-based materials fulfilling the same function, the disposal stage could be a relevant driver of the difference between the compared product systems. For several chemical products and production processes, consistently defining system boundaries is challenging. An example is the application of plant protection products containing chemical pesticide active ingredients (e.g. carbamate insecticides) applied in agricultural crop production, where the FU could be defined to provide a specified amount of crop in a season. Allocating field buffer strips (i.e


carbamate insecticides) applied in agricultural crop production, where the FU could be defined to provide a specified amount of crop in a season. Allocating field buffer strips (i.e. non-agricultural areas that are among other functions introduced to reduce the impact of applied pesticides on non-treated areas), which may be required by law, to the technosphere would apparently influence the crop yield per hectare and amount of pesticide used compared with an equivalent system, where the buffer strips are defined as part of the environment (Rosenbaum et al. 2015). Including buffer strips in the considered technosphere system or not will, hence, influence the related impacts and also defines the scope of the environmental distribution processes of pesticides in the LCI and LCIA phases


As a consequence, the definition of the system boundaries needs to be aligned with the selected pesticide inventory data and characterisation models to avoid overlaps, double counting of processes and potential gaps along the pesticide impact pathways. LCA of Chemicals and Chemical Products


31.3.2 Product System Modelling and Inventory Analysis There are several obstacles that need to be considered in the product system modelling and inventory analysis phase (Chap. 9), after the goal and scope of an LCA study have been defined. Data Availability and Quality All relevant chemical elementary flows from a given product system to the environment need to be quantified in the LCI phase. When using LCA software, emission quantities are often available through an LCI database, for example for processes occurring in Europe or the ‘rest of the world.’ LCI databases generally rely on typical or average emission inventories or an inventory taken by one industry for a given unit process, which may be outdated or tied to, e.g. a specified electricity mix. Thus, it is always preferred to gather primary data, especially for the foreground system modelling (Chap. 9), of the specific LCA case under study


a specified electricity mix. Thus, it is always preferred to gather primary data, especially for the foreground system modelling (Chap. 9), of the specific LCA case under study. This poses a particular challenge to LCA practitioners, who may or may not have access to company-specific data to resolve the nuances of a particular supply chain. While in some cases, a particular commissioner of an LCA study might provide such data, while in other cases such data have to be collected from different parties. An example is the application of plant protection products, where pesticide manufacturers will know the concentration of a pesticide active ingredient in a formulation product, but where the different farmers might know the effectively used amount that is applied on agricultural fields and this usually depends on pest-, climate-, soiland application-specific conditions


Emission Estimation and Modelling Most chemical synthesis and material or product manufacturing processes involve several steps, which can yield usable by-products that have to be considered in an LCA study (see Chap. 9 for further details). As an example, harvesting sugar cane yields refined sugar, but also molasses (sugar refining by-product) and bagasse (dry leftover biomass after extracting the juice from the sugar cane). While molasses can be further used to produce biochemicals, bagasse is usually burned (for energy conversion) or used as livestock feed (Fig. 31.5). In an LCA study, usually only one of these products (sugar, biochemical, energy, livestock feed) is in focus and the other products must be accounted for through subdivision or system expansion or, if it cannot be avoided, through different types of allocation (see Chap. 9). When building a product system model, different tools and software packages are available


9). When building a product system model, different tools and software packages are available. Specifically for simulating material and energy balances of chemical production and processing, there exist several (open-source and commercial) chemical process simulators, such as Aspen HYSYS for oil and gas process simulation and Aspen Plus for chemical process optimisation (www.aspentech.com), BatchReactor and BatchColumn for chemical reactor and batch distillation columns simulation, respectively (www.prosim.net), or the CHEMCAD software suite for chemical process simulation and optimisation including batch operations (http:// www.chemstations.com). Such software packages may include proprietary data P. Fantke and A. Ernstoff


from the chemical and other industries that are otherwise not accessible and may intrinsically use different allocation systems. The responsibility of ensuring transparency and consistency when building a product system including proper consideration of co-products and by-products lies with the LCA practitioner. However, several documents exist for LCA practitioners to seek guidance, and working examples of co-product considerations for the chemical industry can be found elsewhere (e.g. Weidema 2000; Karka et al. 2015). In most LCA studies, an inventory covers hundreds of processes and emission flows but not all chemical emissions are usually able to be considered. Often missing from LCI databases are, e.g. emissions to the occupational and consumer environments, and the ingredients (e.g. chemicals) in a product, which can be emitted indoors during product use or outdoors during post-use as demonstrated in Example 31.2. Example 31.2 LCI Emission Pathways When a consumer product (e.g


chemicals) in a product, which can be emitted indoors during product use or outdoors during post-use as demonstrated in Example 31.2. Example 31.2 LCI Emission Pathways When a consumer product (e.g. perfume) or industrial product (e.g. agricultural pesticide) is used, the chemicals within the product can follow different transport and fate mechanisms that can lead to exposures of humans and ecosystems. Consider that a colleague at work applies an air freshener or perfume in the office. Perhaps you smell or even taste it in the first minutes after application (indication of exposure), maybe the scent remains in the office for some days (indicating sorption and desorption to and from indoor walls and other surfaces), and maybe you can even smell it just outside the office building (indicating transport outdoors via ventilation)


In some cases, a large proportion of chemicals within products can be taken in by humans during and after product use, which is a major concern amongst regulators and researchers. In LCA, such considerations are currently largely missing, but first efforts were made to include indoor fate and exposure pathways (referred to as ‘near-field’) into the toxicity characterisation model USEtox 2.0 (http://usetox. org). Without accounting for near-field fate and exposure pathways, LCA studies typically may assume a fixed fraction like 100% of product ingredients being emitted to the environment. In general, assuming such emission distributions could lead to an underestimation of resulting human toxicity potentials and in some cases also to an overestimation of environmental or ecosystem impacts. This is illustrated in Fig


This is illustrated in Fig. 31.6 for d-limonene as commonly found chemical in a shampoo product, where assuming 100% of the used product being washed-off (left-side pathway in figure) instead of modelling a more complex yet more realistic distribution (right-side pathway in figure) yields a difference of more than three orders of magnitude for freshwater ecotoxicity impacts, which is beyond the uncertainty range for this impact category. Emissions can also occur from chemical residues in products that are related to cross-contamination, i.e. such chemicals are not purposefully added to a product and enter a product from using, e.g. recycled material where not all chemical ingredients are known. Often, inventory data related to cross-contamination LCA of Chemicals and Chemical Products


pathways are very limited if at all available. Using similar processes or pathways as proxy might be a possibility to address this limitation, but also introduces additional uncertainty in the emission estimates. Spatiotemporal Variability in Emissions Time (e.g. year, season or duration) and location can influence variations in emissions, referred to as ‘spatiotemporal’ variability. In many cases, LCI results do not capture the time of emissions from systems, e.g. agricultural practices (e.g. harvesting, applying fertilisers) can occur according to daily or seasonal cycles according to the geographic location of the farm. Likewise, emissions of landfill leachate are influenced by changes in environmental conditions (e.g. acidity and temperature) which can change through time and according to location (Bakas et al. 2015). Incomplete Emission Inventories It is important to be aware of the incompleteness of some emission inventories


2015). Incomplete Emission Inventories It is important to be aware of the incompleteness of some emission inventories. For example, energy conversion processes generally are well detailed in LCI which can result in high toxicity related impacts resulting from energy conversion, but other processes, e.g. related to chemical synthesis may have less complete inventories and, hence, related toxicity impacts might be underestimated (Laurent et al. 2012)


related to chemical synthesis may have less complete inventories and, hence, related toxicity impacts might be underestimated (Laurent et al. 2012). Ecotoxicity impacts: 3×10−4 PAF m3 d 100% washed-off 3×10−6 kg to freshwater 3×10−5 kg to air 1.5×10−5 kg to soil 20% user intake 80% volaƟlizaƟon <1% washed-off Freshwater aquaƟc ecosystem toxicity characterizaƟon factors [PAF m3 d kg−1] 3×10−10 kg to freshwater 4×10−5 kg to air 2×10−9 kg to soil Ecotoxicity impacts: 2×10−7 PAF m3 d 5 g of product applied daily 1% d-limonene w/w content 0.05 g of d-limonene applied <1% biodegraded 5% to freshwater 65% to air 30% to soil <1% biodegraded 5% to freshwater 65% to air 30% to soil environmental emissions aŌer treatment % washed-off to wastewater treatment plant % volaƟlized released to air no near-field pathways considered including near-field pathways Fig


31.6 Illustrative example of assumptions for emission distributions of chemicals in consumer products in life cycle assessment showing a substantial decrease in the estimated potentially affected fraction (PAF) of freshwater species from the chemical ingredient d-limonene (CAS: 5989-27-5) in shampoo when accounting for indoor fate and exposure of cosmetic products. Adapted from results from Ernstoff et al. (2016a) combined with freshwater ecotoxicity characterisation factors from USEtox 2.0 (http://usetox.org). Air emissions were assumed to be to urban air, water emissions to continental freshwater, and soil emissions to continental natural soil P. Fantke and A. Ernstoff


31.3.3 Impact Assessment Characterising chemical emission flows resulting from the LCI in terms of their impacts on humans and the environment requires a careful consideration of study context (e.g. spatial region), number and relevance of chemicals to be characterised (in many cases, most chemicals contribute marginally to overall impacts, while only few chemicals dominate overall impact profiles). In the following, challenges and pitfalls in the impact assessment of chemical products and processes are discussed with focus on toxicity-related impacts, where special challenges exist mainly due to the countless chemicals to be characterised and the complexity of related impact pathways. Limited Substance Coverage USEtox, a scientific consensus model for characterising human and ecotoxicological impacts of chemicals, presently provides characterisation factors for more than 3000 chemicals, which constitutes the largest list currently available in LCIA


However, with tens of thousands of chemicals on the market, inventoried chemical emissions either documented in an LCI database or by a practitioner investigating a specific system or process, may in many cases not have existing characterisation factors or the data required to develop new characterisation factors (e.g. toxicity dose-response information). This limitation to substance coverage in LCIA is important when interpreting results, because a lack of data for many chemicals does not preclude their possible impacts. Chemical Degradation Products When a chemical does not degrade, or degrades very slowly, it is considered ‘persistent.’ Persistent chemicals thereby can be linked to greater impacts because they are not or very slowly removed from the system through degradation. In current LCIA methodologies, abiotic (e.g. where a chemical is transformed via interactions with sunlight) and biotic (e.g


In current LCIA methodologies, abiotic (e.g. where a chemical is transformed via interactions with sunlight) and biotic (e.g. when a chemical is metabolised by soil bacteria) degradation essentially ‘removes’ organic chemicals from the system and no further impacts are characterised. In reality, degradation processes transform a chemical into one or more degradation or transformation ‘products’, including other chemicals or gases, which can also impact the environment. Degradation products can have greater or lesser impacts than their parent compounds; for example aminomethylphosphonic acid (AMPA), which is the main degradation product of the broad-spectrum herbicide glyphosate, is more persistent and more toxic than the glyphosate parent compound. As an example, not including AMPA in an LCA study that considers agricultural processes where this herbicide is used could underestimate the impacts of using glyphosate


As an example, not including AMPA in an LCA study that considers agricultural processes where this herbicide is used could underestimate the impacts of using glyphosate. Therefore, an LCA practitioner should include estimates of persistent degradation products and appropriate characterisation factors (in this case for AMPA, not glyphosate) to better capture the impacts of chemicals. While this approach will not be feasible for all chemicals (due to data limitations), it should be performed when the issue is known and data are available. LCA of Chemicals and Chemical Products


Impacts from Chemical Mixtures Impacts towards humans or different ecosystems, related to chemical emissions, are a function of the simultaneous prevalence of other chemicals, which might have synergistic (enhancing) or antagonistic (counteracting) properties with respect to the effect of a considered chemical. Since information on the site-specific mixture of chemicals in any environmental medium or compartment is not usually available, and the impacts of such a mixture on humans or the environment are not known, synergistic or antagonistic effects are usually not considered, and instead additivity of exposure and related effects is assumed. This means that the effects of all chemicals contributing to the same impact category, e.g. freshwater aquatic ecosystem toxicity or ozone depletion, are summed up to arrive at an overall product system-related impact score


freshwater aquatic ecosystem toxicity or ozone depletion, are summed up to arrive at an overall product system-related impact score. If for any LCA study the emission location and time is known and related background levels are available for all relevant chemicals, this assumption could be evaluated by identifying and quantifying the synergistic or antagonistic effect potentials. However, the potentially added accuracy in an LCA context is most likely not relevant given existing uncertainty attributable to other aspects in the characterisation of chemical emissions. Besides, the large number of chemicals present and emitted into the environment yields an almost limitless amount of possible mixtures, rendering it impossible to quantify the specific effect potentials for each mixture


Missing Fate and Exposure Processes and Pathways In order to reduce the demand put on LCA practitioners, to streamline workflows, and to allow for science-based and consensus-driven solutions, LCIA often relies on predefined methodologies. While hundreds or even thousands of chemicals might be inventoried for various processes in an LCA study, characterisation factors or a LCIA method for a given impact at mid-point or end-point level might in some cases be missing, especially for toxicity-related impacts (see Chap. 10). Moreover, certain exposure settings (occupational, consumer) or routes (e.g. dermal exposure) or target organisms (e.g. exposures of bees) may be missing from an LCIA model. Effect factors may also be missing or inconsistent, e.g. in the case of human toxicity, effects of allergy or endocrine disruption (i.e. interaction with the hormone system) are often not included, but may be highly relevant for chemicals in consumer products


interaction with the hormone system) are often not included, but may be highly relevant for chemicals in consumer products. Finally, many of the methodological gaps in LCIA are also due to the reliance on simplifying assumptions. The LCA practitioner, who is constrained by resources (time, money, data access), is responsible for compiling the necessary data and for ensuring that the LCIA methodology chosen (or developed) is suitable for the defined goal and scope of an LCA study. Specifically, to characterise a chemical’s impact, several assessment factors are required and must be sufficiently scrutinised within the chosen LCIA method, such as the chemical environmental fate, ecosystem and/or human exposure if relevant, and subsequent effects with respect to given impact categories. Each of the related data requirements poses its own challenges


Each of the related data requirements poses its own challenges. To avoid the misleading conclusion that missing aspects of the chosen LCIA method do not cause impacts because they were not assessed, it is important to be familiar with which processes (e.g. biotransformation), environmental compartments (e.g. indoor air), exposure P. Fantke and A. Ernstoff


pathways (e.g. dermal uptake), and effects (e.g. endocrine disruption), may be missing from the selected characterisation methods but are relevant for the system under study. In some cases such missing aspects can be addressed by the practitioner by developing new methods or by adapting existing methods; if not, it is important to be aware of how this could influence results. Spatiotemporal Variability of Impacts LCIA methods are generally based on regional or global averages for various chemical, environmental and pathway data and processes, e.g. how long it takes chemicals emitted to freshwater to reach the sea (i.e. residence time), or how many persons live in an urban area (i.e. population density). Studies have shown, intuitively, using a continental average instead of ‘spatially differentiated’ regionalised models can yield large uncertainty in the estimated impacts (e.g. Kounina et al. 2014). Thus, if the location of the emissions (e.g


Kounina et al. 2014). Thus, if the location of the emissions (e.g. from a specific factory) in an LCA study is known, using a model with characterisation factors specific for that region can reduce uncertainty of model results. If emission locations are not known (as is the case for most chemicals in typical LCA studies), characterisation results for regions can be applied that are parameterised, i.e. averaged for the characteristics of a particular region. The same rule applies for temporal aspects, where in LCA mostly steady-state conditions and continuous emissions are assumed, which might not be true for, e.g. agricultural pesticides that are applied on specific days only (i.e. pulse emissions). In such cases, accounting for the dynamics of the chemicals in the modelled environmental system may reduce uncertainty in characterisation results (e.g. Fantke et al


pulse emissions). In such cases, accounting for the dynamics of the chemicals in the modelled environmental system may reduce uncertainty in characterisation results (e.g. Fantke et al. 2012), but whenever temporal information on emission patterns is not available, parameterised characterisation results can be applied that account for the most important temporal aspects of a modelled system. Impacts Versus Benefits Life cycle impact assessment inherently focuses on quantifying ‘negative’ impacts on humans and the environment. A stakeholder could in some cases argue that their product or service offers a benefit to society that is not accounted for, meaning that an LCA yields misleading results. When facing such an argument as an LCA practitioner, it is important to go back to the fundamentals of LCA


When facing such an argument as an LCA practitioner, it is important to go back to the fundamentals of LCA. The impact assessment phase of LCA is designed to assess environmental ‘benefits’ in the form of ‘avoiding environmental impacts.’ For example, a wastewater treatment plant design that also decreases environmental pollution compared to another design offers a ‘benefit’ that is quantifiable in an LCA context (see Chap. 34 on LCA of wastewater treatment). Furthermore, when comparing functionally equivalent products or services, their benefits (e.g. restoring a wetland to yield a level of biodiversity, or designing a car with a certain safety rating) is often captured in the functional unit of an LCA study, which defines a unit of the (beneficial) service being provided. There are special cases where considering societal benefits that are not captured in the functional unit or by the assessment methods can be extremely important when guiding decision-making


There are special cases where considering societal benefits that are not captured in the functional unit or by the assessment methods can be extremely important when guiding decision-making. In some cases, LCA may not be the appropriate tool to assess such benefits; however, developing LCA-compatible methods to quantify societal benefits (specifically positive human health outcomes) LCA of Chemicals and Chemical Products


is a topic of high interest when assessing human nutrition and dietary patterns, where two functionally equivalent diets can have very different health impacts or benefits (Nemecek et al. 2016). 31.3.4 Interpretation The interpretation of results is fundamental for the findings and reliability of every LCA study and subsequent guidance provided to stakeholders, and to LCA in general (see Chap. 12). Robust and transparent interpretation of results from an LCA study can offer sound council for the stakeholders and when aggregating with other LCA studies can elucidate generalisable findings important for sustainable development. As an example of nuances of interpretation, the ‘New Plastics Economy’ report (WEF 2016) cites interpretation of several LCA studies and implies that a major shortcoming of LCA is its inability to identify and support ‘target states’, such as moving towards increased production and use of bio-based plastics


Indeed, as previously discussed, LCA studies on bio-based versus fossil-based plastics have demonstrated similar, if not greater impacts (e.g. on land use and toxicity potentials) for bio-based plastics due to agricultural practices (see Chap. 30), which is a finding that may be unintuitive or undesirable to some (e.g. stakeholders in the bioplastics industry). When interpreting such LCA results, it is important to distinguish what an LCA says about ‘here and now’ versus what it could mean for future sustainability goals or targets of stakeholders. For example, LCA results showing bio-based plastics have ‘greater impacts’ than fossil-based plastics do not discredit bio-based plastics as a sustainability goal, but they do indicate that bio-based plastics face sustainability challenges given current agricultural practices, which thus must be addressed to avoid impact trade-offs


Furthermore, LCA results can help indicate which feedstock is the most eco-efficient (less impacts per kilogram) to work towards a bio-based ‘target’. In practice, LCA may not be able to easily identify target states often elucidated according to societal values (which may include socioeconomic or political factors) or intuitive/consensual sustainability goals, but LCA can be instrumental in reaching goals and target states in a holistically sustainable manner and shedding light on challenges faced when working towards such goals. In the following, some additional challenges in interpreting LCA results are outlined. Contribution to Impact Results Especially for LCAs on chemical products or processes, it is important to transparently report and document the contribution of different chemicals to impacts related to product life cycle stages and individual processes. This can help identify potential problems in the processing of LCI or LCIA results (e.g


This can help identify potential problems in the processing of LCI or LCIA results (e.g. if one chemical dominates results). Interpreting LCA results might be particularly challenging if it is not clear whether toxicity-related impacts are associated with chemical emissions occurring along the product life cycle or, in contrast, with chemicals that are P. Fantke and A. Ernstoff


product ingredients (Roos and Peters 2015). As an example, glass used as food packaging can show higher potential toxicity impacts compared with plastic packaging due to transport-related emissions of toxic chemicals from fossil fuel burning (Humbert et al. 2009), which is linked to the fact that glass is usually heavier than plastic. However, plastic food packaging can likely lead to greater exposures to various chemicals through food than glass, but this aspect is not (yet) considered in current LCIA toxicity models (Ernstoff et al. 2016b). Further, it might be unclear whether worker and/or consumer exposure pathways are included as these are currently beyond the scope of LCA studies focusing primarily on environmental emissions. The covered pathways and exposed populations should always be clarified in an LCA study to avoid possible misinterpretation of results


The covered pathways and exposed populations should always be clarified in an LCA study to avoid possible misinterpretation of results. This is of specific relevance for the comparison of chemicals and chemical products and processes, where such ambiguities can cause confusion regarding the contribution of chemicals and related impact pathways and life cycle stages. Identification of Considered Chemicals In any of the aforementioned contexts, it must be acknowledged that most chemicals have various common names (lindane, CAS RN: 58-89-9, is for example also commonly known as HCH, hexachlorobenzene, or cyclohexane, etc.). Hence, it is important to ensure that names for chemicals in the different phases of an LCA study (e.g. inventory analysis and impact assessment) are consistently chosen based on using CAS registry numbers or similar as chemical identifier to, e.g. avoid double counting or neglecting chemicals with ambiguous names


avoid double counting or neglecting chemicals with ambiguous names. This exercise can prove to be challenging as LCA software packages often report chemical inventories by chemical name and not by CAS number. Quality and Uncertainty Quality checks across the large number of inventoried chemicals is usually difficult, but inventory results should nevertheless be verified by, e.g. checking the mass balance of only those chemicals that drive overall impact results, for examples heavy metals that often dominate toxicity impact profiles. Furthermore, it is essential to report and discuss uncertainties of LCA data and results with respect to each impact category as integral part of the analysis, and consider such uncertainties in the interpretation of results and guidance provided to decision makers (see Chap. 11)


11). Particularly uncertainty associated with toxicity characterisation results is high compared with other impact categories and results can furthermore differ between toxicity characterisation methods, which can in some cases influence the overall ranking of compared product systems. Uncertainty (lack of data or understanding) and variability (data heterogeneity) are distinct concepts, but are sometimes (incorrectly) aggregated. For example, often ‘high uncertainty’ is perceived negatively or seen to discredit a particular LCIA method. However, such ‘uncertainty’ can be a direct reflection of reality and variabilities in temporal and spatial chemical fate and organism disease responses (see Chap. 11 for further details)


However, such ‘uncertainty’ can be a direct reflection of reality and variabilities in temporal and spatial chemical fate and organism disease responses (see Chap. 11 for further details). Likewise, if an impact category has low or no associated uncertainty, this is perceived as positive but should in fact be a warning sign that there may be a lack of understanding of what uncertainties and/or variabilities exist or that the environmental relevance (or LCA of Chemicals and Chemical Products


representativeness) of an indicator may be low (which introduces an uncertainty in the interpretation phase, but this is usually not quantified). To begin transparently addressing this issue, impacts should ideally be cross-compared using different LCIA characterisation methods with particular focus on identifying which chemicals contribute the most to impacts in each LCIA method (which are often not the same). Moreover, uncertainty ranges for toxicity-related impacts should be reported in logarithmic scale to put average uncertainties of two to three orders of magnitude into perspective of more than 15 orders of magnitude in the variability across chemicals. This is shown in Fig. 31.7 for 786 chemicals with available measured ecotoxicity effective concentrations for 50% of the exposed species (EC50; mg/L) for aquatic ecosystems. EC50 values are used to calculate effect factors as part of toxicity characterisation in LCIA (see Sect. 10.11)


EC50 values are used to calculate effect factors as part of toxicity characterisation in LCIA (see Sect. 10.11). The relation between uncertainty and cross-chemical variability is not much different for toxicity impacts than for other impacts, where uncertainty in characterisation results (of usually only a handful of contributing chemicals) and related variability across contributing chemicals are both less broad. However, uncertainty ranges vary widely between chemicals, but chemical-specific uncertainty around characterisation factors is usually not available in LCA, except for specific pathways, e.g. exposure to pesticide residues in food crops (Fantke and Jolliet 2016), where also the underlying method to quantify chemical-specific uncertainty is outlined. Comparison with Results from Other Methods Comparing results from an LCA study with results from a different method can help identify methodological inconsistencies that require further inspection


Comparison with Results from Other Methods Comparing results from an LCA study with results from a different method can help identify methodological inconsistencies that require further inspection. As an example, it might be desired to compare the ranking of chemicals in terms of their potential toxicity impacts on humans and/or ecosystems in an LCA study with the ranking of chemicals based on persistence, bioaccumulation and toxicity or other criteria used, e.g. by risk regulators. In this context, it is important to acknowledge that inconsistencies can result from the primary data used in an LCA versus another 1E+05 1E+06 ] uncertai 1E+01 1E+02 1E+03 1E+04 EC50 [mg/L] ncertainty variability 1E-02 1E-01 1E+00 EC50 [m iability 1E-05 1E-04 1E-03 Chemical number Fig


31.7 Ranges of measured chemical-specific ecotoxicity effective concentrations (50% of exposed species affected), EC50, for aquatic ecosystems collected and indicated as reliable for 786 chemicals based on REACH (echa.europa.eu/regulations/reach) P. Fantke and A. Ernstoff


method, or assumptions and cut-offs may be based on different criteria, e.g. worst case versus best estimate or most sensitive species versus average ecosystem sensitivity (Harder et al. 2015). This might lead to problems when comparing chemical rankings based on different assessment methods and data sources. Chemical toxicity results may furthermore differ between regions, countries or assessment methods, and thereby the consideration of chemicals as, e.g. ‘non-carcinogenic’ in LCA toxicity characterisation models may not be consistent with a specific regulatory context, such as the Registration, Evaluation, Authorisation, and Restriction of Chemicals (REACH) legislation framework of the European Union. 31.4 Conclusions Stakeholders commissioning an LCA study can drive the goal and scope, the selection of inventory processes, and the selection of impact categories


31.4 Conclusions Stakeholders commissioning an LCA study can drive the goal and scope, the selection of inventory processes, and the selection of impact categories. In many cases, this can lead to an assessment that is restricted, for example to greenhouse gas emissions and a focus on climate change. The limited scope of such studies must be considered in the interpretation and application of their results, whenever other important impact pathways for chemical production, use, and disposal are not assessed. It is always important to be critical towards LCA outcomes and understand their limitations and scope, and respect that no tool (including LCA) can answer all questions related to chemicals and sustainability. Not only can the scope of an LCA study be intentionally restricted according to its goal and scope, but there are several remaining challenges that also limit LCA, such as partial coverage of chemical inventory data, fate modelling (e.g


regional variation), exposure pathways (e.g. dermal exposure of consumers), and characterisation of potential human and ecosystem toxicity impacts. Given that there are tens of thousands of commercially used chemicals, and often little data on their properties or effects, the challenge of addressing chemical risks and impacts is not unique to LCA. Generally, the various methods for characterising risk and impacts of chemicals face similar challenges of data availability, but they also face methodological challenges and intentional differences. For example, results of an LCA addressing several impact categories and hundreds of chemicals, where often the exact emission location and timing is unknown, are difficult to cross-compare with results of a toxicity-focused risk assessment considering specific (e.g. worst-case) conditions and only one or several chemicals of concern (Harder et al. 2015)


worst-case) conditions and only one or several chemicals of concern (Harder et al. 2015). Attempts of combining LCA with principles of green and sustainable chemistry, combining LCA- and risk-based approaches, and including life cycle impacts in chemical alternatives assessment frameworks demonstrate the growing complementarity and relevance of the life cycle approach in other science-policy fields (Jimenez-Gonzalez and Overcash 2014; Harder et al. 2015; Jacobs et al. 2016). Overall, the number of LCA studies focusing on chemicals or chemical products or LCA of Chemicals and Chemical Products


processes is growing; thus, increasing discourse and trust in LCA methods as well as improving existing inventory and impact characterisation approaches. Over the past years of research, LCA has developed into a powerful tool to identify and assess trade-offs and burden shifting between different environmental issues, identify hotspots and minimise overall environmental impacts of chemicals emitted along the life cycles of products and processes. With rising interest in creating ‘environmentally friendly’ chemicals and products, LCA is particularly important to help avoiding ‘green washing’ and unsupported sustainability claims. A common example is the comparison of products that can be developed purely from petrochemicals and also from a combination of petro- and biochemicals


A common example is the comparison of products that can be developed purely from petrochemicals and also from a combination of petro- and biochemicals. Larger potential greenhouse gas emissions in the petrochemical production are confronted with often larger land use and pesticide-related toxicity impacts from agricultural crop production when serving as feedstock for biochemical production (Tabone et al. 2010; Cespi et al. 2015). Only comparing climate change impacts in this context would lead to false conclusions (i.e. that bio-based chemicals are always ‘greener’) and does not help identify how to optimise production processes and resource use when moving from petrochemicals to biochemicals in, e.g. plastics production. This is especially relevant when assessing emerging technologies, where there is a high level of optimisation potential in the years to come for upscaling lab-level processes to a commercial level


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Int. J. Life Cycle Assess. 18, 431–444 (2013) van Leeuwen, C.J., Vermeire, T.G.: Risk Assessment of Chemicals: An Introduction, 2nd edn. Springer Press, Dordrecht (2007) Van Lieshout, K.G., Bayley, C., Akinlabi, S.O., von Rabenau, L., Dornfeld, D.: Leveraging life cycle assessment to evaluate environmental impacts of green cleaning products. Procedia CIRP 29, 372–377 (2015) Wang, Q., Vural Gürsel, I., Shang, M., Hessel, V.: Life cycle assessment for the direct synthesis of adipic acid in microreactors and benchmarking to the commercial process. Chem. Eng. J. 234, 300–311 (2013) WBCSD: Life Cycle Metrics for Chemical Products, p. 120. World Business Council for Sustainable Development, Geneva (2014) WEF: The New Plastics Economy: Rethinking the Future of Plastics, p. 36. World Economic Forum, Geneva (2016) Weidema, B.: Avoiding co-product allocation in life-cycle assessment. J. Ind. Ecol


36. World Economic Forum, Geneva (2016) Weidema, B.: Avoiding co-product allocation in life-cycle assessment. J. Ind. Ecol. 4, 11–33 (2000) Wernet, G., Conradt, S., Isenring, H.P., Jiménez-González, C., Hungerbühler, K.: Life cycle assessment of fine chemical production: a case study of pharmaceutical synthesis. Int. J. Life Cycle Assess. 15, 294–303 (2010) Whittaker, M.H.: Risk assessment and alternatives assessment: comparing two methodologies. Risk Anal. 35, 2129–2136 (2015) Yuan, G., Peng, H., Huang, C., Hu, J.: Ubiquitous occurrence of fluorotelomer alcohols in eco-friendly paper-made food-contact materials and their implication for human exposure. Environ. Sci. Technol. (2016). doi:10.1021/acs.est.1025b03806 Zhang, Y., Bakshi, B.R., Demessie, E.S.: Life cycle assessment of an ionic liquid versus molecular solvents and their applications. Environ. Sci. Technol


(2016). doi:10.1021/acs.est.1025b03806 Zhang, Y., Bakshi, B.R., Demessie, E.S.: Life cycle assessment of an ionic liquid versus molecular solvents and their applications. Environ. Sci. Technol. 42, 1724–1730 (2008) Author Biographies Peter Fantke develops methods for LCIA, health impact assessment and chemical alternatives assessment since 2006. Has contributed to UNEP/SETAC LCIA working groups and is USEtox Manager. He is mainly interested in quantifying and characterising chemical emissions, uncertainty analysis, consumer exposure, chemical substitution and model parameterisation. Alexi Ernstoff Studied various aspects of chemical fate, transport, and exposure since 2007. Recent focus is modelling human exposure to chemicals in products for LCIA. Main interest is ensuring human health impacts, mediated by using consumer and food products, is consistently considered in quantitative sustainability assessments. LCA of Chemicals and Chemical Products


Chapter 32 LCA of Nanomaterials Mirko Miseljic and Stig Irving Olsen Abstract Application of nanomaterials in products has led to an increase in number of nanoproducts introduced to the consumer market. However, along with new and improved products, there is a concern about the potential life cycle environmental impacts. Life cycle assessment is able to include a wide range of environmental impacts but, due to data limitations, it is commonly applied with focus on the cradle-to-gate part of the nanoproducts life cycle, neglecting use and disposal of the products. These studies conclude that nanomaterials are more energy demanding and have an inferior environmental profile than conventionally used materials, but functional units of these comparisons need to consider the use stage benefits attained through nanomaterials


A particular assessment challenge is the lack of understanding of the toxicological mechanisms related to potential release, fate and effects of nanomaterials when penetrating into living organisms. This is especially relevant for the freshwater compartment, as it is a common recipient. 32.1 The basis of the nano-technology terminology is the nanometre, which is one billionth of a metre (10−9 m). Nano-scale is defined as the range from 0.1 to 1000 nm, nanomaterial as a material with at least one dimension within 1–100 nm, and nanoparticle as a particle with all three dimensions within the 1–100 nm range (ISO 2008; SCENIHR 2007). Nanomaterials and nanoparticles can be naturally or unintentionally produced, and they have always been present in the human surroundings. Examples of these are, e.g., soil (dust) and salt particles in air, to which humans are commonly exposed. These naturally occurring nanomaterials are in general known to cause little harm to humans (Buzea et al. 2007)


These naturally occurring nanomaterials are in general known to cause little harm to humans (Buzea et al. 2007). Other sources of naturally occurring nanoparticles are, e.g. forest fires or volcanos, the particles from M. Miseljic (&)  S.I. Olsen Division for Quantitative Sustainability Assessment, Department of Management Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark e-mail: mimi@dti.dk © Springer International Publishing AG 2018 M.Z. Hauschild et al. (eds.), Life Cycle Assessment, DOI 10.1007/978-3-319-56475-3_32


which may have the same impacts as the anthropogenic, unintentionally produced particles arising from combustion activities. The other path to production of nanomaterials is by manufacturing activities through either downscaling (from bulk) or upscaling (from atomic or molecular) materials. These activities may also cause unintentional emissions of nanoparticles. The manufacturing of engineered nanomaterials (ENMs) is linked to application of nanomaterials in/on consumer products, as alteration of materials from bulk to nano-size leads to an increase in surface area and improved functionality. As an example, gold becomes reactive at nano-size and may be used to increase oxidation in car catalytic converters so emission of pollutants is reduced. Further, ENMs may also provide products with, e.g. improved material properties like hydrophobicity (lotus effect), strength and/or electrical conductivity


Further, ENMs may also provide products with, e.g. improved material properties like hydrophobicity (lotus effect), strength and/or electrical conductivity. For quite some time particles from combustion have been known to cause harmful human impacts, even though the exact mechanisms are still being researched. ENMs and nanoparticles (ENPs) also cause concerns, but their behaviour in the environment and potential impacts to environment and humans are to a large extent still unknown (Miseljic and Olsen 2014; Jolliet et al. 2013). In order to embrace the entire life cycle of ENMs, and to avoid burden shifting, the LCA approach is favourable in order to quantify potential environmental impacts—not only as a single approach but also as a framework to be applied along with other methods (Som et al. 2010). 32.1.1 Nanoproducts and Environmental Assessment Nano-technology and the application of ENMs in products have developed much in recent years


2010). 32.1.1 Nanoproducts and Environmental Assessment Nano-technology and the application of ENMs in products have developed much in recent years. The reason for this is that ENMs are able to improve properties and functionalities of different materials, and thereby the consumer products. This has meant that companies have developed new and smart products, resulting in more nanoproducts being introduced to the consumer market (see Figs. 32.1 and 32.2). The more common nanoproducts are within the product category of health and fitness, with TiO2-enhanced sunblock and Ag-enhanced clothing as prominent examples. The home and garden product category contains the second most widely used nanoproducts (mainly as sealants and coatings), as seen in Fig. 32.1. The most commonly used nanomaterials are based on Ag, TiO2, and carbon as shown in Fig. 32.2


32.1. The most commonly used nanomaterials are based on Ag, TiO2, and carbon as shown in Fig. 32.2. In line with the rapid introduction of nanoproducts, and thereby ENMs, concerns are raised in terms of the potential environmental impacts these may have along their life cycle. Currently, chemical risk assessment (RA) is commonly performed on ENMs, but this approach has a different scope compared to LCA. RA is a procedure applied in order to estimate if a toxicological risk occurs from a substance, thus calculating, measuring or modelling the existence of risk derived from chemical exposure (European commission 2007; ECHA 2010, see also Sect. 31.1.3). LCA on M. Miseljic and S.I. Olsen


ENM products per category Product categories Appliances Automotive Cross cutting Electronics and computers Food and beverages Goods for children Health and fitness Home and garden Number of products November, 2007 August, 2014 Fig. 32.1 Number of ENM products per product category (PoEN 2014; Hansen 2009; Miseljic 2014) ENM products per material Product categories Carbon (incl. fullerenes & nanotubes) Gold Magnesium Silicon Silicon dioxide Silver Titanium dioxide Zinc oxide Number of products November, 2007 August, 2014 Fig. 32.2 ENM products per most frequently used material, in year 2007 and 2014 (PoEN 2014; Hansen 2009; Miseljic 2014) LCA of Nanomaterials


the other hand is a relative environmental impact assessment method that also considers a wide range of environmental impact categories (ISO 2006). In LCA, the starting point of the assessment is the functional unit, where products or systems are studied, whereas RA is commonly substance oriented, see also Fig. 32.3. There are benefits and challenges to applying both RA and LCA, due to these two approaches being developed with different initial purposes. The benefit of applying RA for ENMs is that it targets specific emission, exposure and dose-response conditions, but the downside is that the uncertainty is still high due to lack of data and lack of proper regulation. A refinement of regulation is needed, e.g. for the characterisation of ENMs applied in laboratory testing and thereby the use of appropriate metrics for expressing hazard and exposure. In addition to ENM mass, Aitken et al. (2011) and Hankin et al


In addition to ENM mass, Aitken et al. (2011) and Hankin et al. (2011) proposed particle number and surface area as additional characteristic metrics. LCA conveys an assessment of a wider range of environmental impacts, but as for RA there are also challenges to this approach. One of these is, as for RA, the lack of needed data and another is the lack of consideration of impact from ENM/ENP released to the environment. Sweet and Strohm (2006) and Som et al. (2010) outlined that RA should consider more life cycle concepts and LCA should be more risk-based, when dealing with future environmental impact assessment of ENMs. This underlines the general approach that LCA should be applied as a complimentary framework along with other environmental impact assessment approaches (Grieger et al. 2012)


This underlines the general approach that LCA should be applied as a complimentary framework along with other environmental impact assessment approaches (Grieger et al. 2012). ExtracƟon of raw materials ProducƟon of material and/or product Use Disposal Transport Transport Transport LCA Starting point: ”functional unit” for product or system Risk assessment Starting point: ”substance” Use scenarios Absolute results Relative results Life cycle Fig. 32.3 RA and LCA coverage and starting point when addressing a product or system (Grieger et al. 2012) M. Miseljic and S.I. Olsen


32.2 Literature Review In order to evaluate the current assessment status of ENMs, and the application of LCA in this context, published scientific articles were reviewed. Only 29 LCA case studies were found and reviewed according to strengths and weaknesses, and the challenges the assessments represent (Miseljic and Olsen 2014). Jolliet et al. (2013) identified 43 studies but use a broader definition of LCA. Some studies did not perform LCA according to usual guidelines (e.g. ISO 2006), but claimed to include life cycle thinking and commonly focused on energy consumption of ENM manufacturing. 32.2.1 Impact Hotspots Commonly, among the reviewed studies in Miseljic and Olsen (2014) and Jolliet et al. (2013), it can be concluded that, on a same produced mass basis, the manufacture of nanomaterials is found to have significantly higher energy requirements compared to the production of conventional materials such as aluminum or steel (shown e.g. in Khanna et al. 2007, 2008)


in Khanna et al. 2007, 2008). Even though there are discrepancies of up to several orders of magnitude between the energy requirements for the same nanomaterials reported by different studies this often results in a less favourable cradle-to-gate environmental impact profile of ENM products compared to comparable conventional products. The high energy demand for production emphasise that one should be careful with normally accepted cut-off criteria when making inventories of nanoproducts since the product generally contains only few percent of ENMs. In terms of impact categories, those that are dominating for ENM production are nonrenewable resource depletion, global warming, acidification and impacts caused by airborne inorganics. However, due to the improved product functionality, the use stage of ENM products is often more environmentally friendly than for conventional products. This is exemplified In Lloyd and Lave (2003) and Lloyd et al


This is exemplified In Lloyd and Lave (2003) and Lloyd et al. (2005), where ENM enhanced clay-polypropylene ENMs in car body panels and platinum-group metal particles in car catalysts have an environmentally friendlier use stage. However, potential release of ENMs/ENPs to the environment is not considered in these studies. Most of the reviewed LCA studies focused on the manufacturing of nanomaterials, thus limiting the assessment scope to the energy consumption of the cradle-to-gate part. The use and disposal stages are commonly not considered, with few exceptions, e.g. Walser et al. (2011) who also consider toxicity from ENMs/ENPs from nanoproducts. However, the tendency is also that these studies rely on generic data, as seen in e.g. Grubb and Bakshi (2010), Osterwalder et al. (2006), Tibbetts et al. (1994), Hwang et al. (2005), Healy (2006), and Healy et al. (2008). LCA of Nanomaterials


32.2.2 Overall Findings The tendencies across the identified LCAs on ENMs can be summed up and also reflect the LCA state of knowledge regarding ENMs (Miseljic and Olsen 2014): • Usually the studies consider cradle-to-gate or manufacturing system boundary. • Use and disposal stages of the life cycle are poorly covered. • Functional unit does not always consider functional benefits of ENMs. • Common use of generic LCI data and assumptions, e.g. for upscaling of laboratory data. • Almost no consideration of ENM release (e.g. in use or disposal stages) and the potential toxic impacts from these (fate, exposure and effect consideration). Walser et al. (2011) and Meyer et al. (2010) are exceptions. • Cradle-to-gate LCA comparison of counterpart products (with ENMs and without) show that ENM products are more energy demanding and therefore have a worse cradle-to-gate environmental profile, e.g. in polymer nanocomposites versus steel and socks with and without nano (Moign et al


in polymer nanocomposites versus steel and socks with and without nano (Moign et al. 2010; Meyer et al. 2010). • Cradle-to-grave LCA comparison of counterpart products (with ENMs and without) show that the use stage is better for ENM products as usually an improved functionality is achieved, e.g. comparing clay-propylene nanocomposites with steel or aluminium in light duty vehicles (Osterwalder et al. 2006). End-of-life performance of ENM products is rarely considered. 32.3 General Methodological Issues 32.3.1 Goal and Scope Definition Generally, it is found that the system boundaries for a fair comparison to products containing ENMs should include both the use stage where the beneficial functionalities of the ENMs are expressed, and the disposal stage. Certain reviewed studies consider the use and disposal stages, but their coverage is rather incomplete (Lloyd and Lave 2003; Lloyd et al. 2005; Babaizadeh and Hassan 2012; Manda et al. 2012; Roes et al. 2007; Steinfeldt et al. 2010)


2005; Babaizadeh and Hassan 2012; Manda et al. 2012; Roes et al. 2007; Steinfeldt et al. 2010). In the quoted example by Lloyd and Lave (2003) on clay-polypropylene ENMs in car body panels, the use stage is assessed by solely including the resource savings (fuel consumption) when driving with the lighter ENM enhanced panels. Other supplementary materials are not considered, meaning that release of other agents is not considered in the use stage (Som et al. 2010). In addition, the disposal stage is commonly not dealt with due to lack of knowledge in end-of-life treatment of ENMs and also which disposal processes they will be subject to (landfilling, incineration, or recycling). M. Miseljic and S.I. Olsen


In order to perform a comparable LCA the functional unit is central, as a comparable functionality of products or systems needs to be applied. Certain ENM studies tend to apply a simplified mass based functional unit, e.g. relating to 1 kg of an ENM product (Joshi 2008; Kushnir and Sandén 2008; Grubb and Bakshi 2010). However, a mass-based functional unit does not make sense when comparing ENMs with conventional products, as functionality is not proportional with weight (Hischier and Walser 2012). The improved material functionality, when using ENMs in products, needs to be considered in the functional unit. This means that higher resource and energy use in ENM production, compared to conventional additives, may be justified in the use stage by leading to less environmental impacts. As an example, Roes et al


As an example, Roes et al. (2007) include elasticity (Young modulus) and tensile strength in the functional unit when comparing polypropylene (PP)/layered silicate nanocomposites with conventional PP, reason being that the nanocomposite and PP mix obtains the needed material properties at a lower weight. In this approach Roes et al. (2007) scale the functional unit, but in general it may be difficult to identify the most important properties that are to be applied in a functional unit of a fair LCA comparison. 32.3.2 Inventory Data The inventory modelling of a nanoproduct typically involves background processes that are not specific to nano and thus not of specific interest here, while foreground process (e.g. data for production of ENM; and direct nanoparticle emissions) should be specific of the considered process (Jolliet et al. 2013). Commonly, as identified in the 29 LCA on ENMs studies from Miseljic and Olsen (2014), the studies rely on generic data for production of ENMs


2013). Commonly, as identified in the 29 LCA on ENMs studies from Miseljic and Olsen (2014), the studies rely on generic data for production of ENMs. Primary process data, on the other hand, is often difficult to acquire from the ENM industry. This can be due to several factors, but mainly to the relative novelty of the scientific field and the competition within the technology domain. This ongoing tendency leads to a higher level of uncertainty, as generic data and estimations need to be applied (e.g. in Bauer et al. 2008; Joshi 2008; Merugula et al. 2010; Isaacs et al. 2006). The immaturity of the field also lies behind the incomplete life cycle coverage in the performed LCAs with the very frequent omission of use and disposal stages from the product system modelling. Life cycle inventory (LCI) modelling is gradually improving for ENMs, studies such as Geranio et al. (2009), Köhler et al. (2008), Künninger et al. (2010), Som et al. (2011), Suppen et al. (2005), Durucan et al


(2009), Köhler et al. (2008), Künninger et al. (2010), Som et al. (2011), Suppen et al. (2005), Durucan et al. (2006) and Gutowski et al. (2010) do not perform LCAs per se, but are providing valuable LCI data on specific processes as well as some estimates of the direct release of nanoparticles. Geranio et al. (2009) quantifies the release of Ag ENPs during textile washing and Künniger et al. (2010) the release of nano-Ag from facades due to weathering. LCA of Nanomaterials


Concerning the direct emissions of nanoparticles, the physical and chemical characteristics of the emitted nanoparticles are also of interest since these characteristics are needed to link up with the impact assessment. 32.3.3 Impact Assessment Generally, the impact from background and foreground process is no different from other LCAs. Therefore, the main aspect to consider is the potential impacts from direct emissions of nanoparticles. Here the potential toxic effects have caused highest concern and the next chapter will deal exclusively with this. 32.3.4 Interpretation It is evident from the previous text that LCAs of nanoproducts are rather uncertain and that an understanding of the main uncertainties is important (Jolliet et al. 2013). It is suggested to use tools for uncertainty assessment and sensitivity analysis as explained in Chap. 11


2013). It is suggested to use tools for uncertainty assessment and sensitivity analysis as explained in Chap. 11. 32.4 Specific Methodological Issues for Ecotoxicological Impact Characterisation of ENMs/ENPs As concluded in the previous section, the potential release of ENMs throughout the life cycle of ENM products is generally not considered. A sensitivity analysis of the importance of potential freshwater ecotoxicity impacts from Ag and TiO2 ENM release from products underlined the need to consider potential impacts of such releases throughout a product’s entire life cycle (Miseljic and Olsen 2014). The analysis also illustrates the differences between the impacts of different ENPs (i.e. higher freshwater ecotoxicity from Ag ENMs, compared to TiO2 ENMs). The assessment of impacts of ENM release from cradle to grave improve the LCA of ENMs, and address an increasing environmental concern (Buzea et al. 2007; Bauer et al. 2008; Oberdörster et al. 2007; Jolliet et al. 2013)


2007; Bauer et al. 2008; Oberdörster et al. 2007; Jolliet et al. 2013). However, current lack of understanding of the mechanisms leading to toxicity, more precisely the ENM release, fate and potential effects when penetrating into living organisms, pose challenges for the assessment while being highly relevant; especially for the freshwater compartment, as it is a common recipient (Quik et al. 2011; Lowry et al. 2012; Som et al. 2010). According to Jones and Grainger (2009), the main hurdle is to predict the actual fate of the released ENMs. In the following, the most important aspects related to such an assessment are discussed as an example of the challenges specific for LCIA of nano-technology. M. Miseljic and S.I. Olsen


32.4.1 Particle Appearance The primary ENP appearance differs over time and according to environmental conditions, as ENPs tend to agglomerate (coagulate), aggregate (fuse) or a combination thereof. The interaction and bonding happens in order to reduce the high-surface energy. Interaction between two ENPs, e.g. in liquid and air, can in theory be described by forces of van der Walls attractions and electrostatic repulsions (Rupasinghe R-A-TP 2011). The appearance of ENPs, along with the forces causing this, influences the toxicity of ENMs in e.g. water (Oberdörster et al. 2007). 32.4.2 Transformation After release to freshwater, which is considered a common recipient, the ENMs are either subject to biotic (interaction with plants, water flea, fish etc.) or abiotic (interaction with water, sand, light, etc.) transformation. These can alter the shape, size, surface chemistry, and ultimately the fate of the released ENMs


These can alter the shape, size, surface chemistry, and ultimately the fate of the released ENMs. Thus, physico-chemical properties of ENMs are important for the differentiated behaviour of ENMs in water and the water-phase processes considered important are: • Aggregation/agglomeration • Dissolution • Sedimentation (and resuspension) • Change in surface structure of ENMs/ENPs. Commonly, freshwater fate of ENMs tends to be dominated by sorption to high-surface-area colloids with subsequent sedimentation (Klaine et al. 2008). Within the sediment the ENMs can be transported, and also re-suspended to the water phase, see Fig. 32.4. 32.4.3 Transport Transport of ENMs is partially controlled by aggregation/agglomeration, which subsequently is followed by sedimentation. The aggregation will depend on parameters such as (Lowry and Casman 2009; Lowry et al. 2012): • Hydrophobicity • Chemical bonding between nanoparticles • Ionic strength • Ionic composition


The aggregation will depend on parameters such as (Lowry and Casman 2009; Lowry et al. 2012): • Hydrophobicity • Chemical bonding between nanoparticles • Ionic strength • Ionic composition. Ionic strength, pH and the presence of divalent cations such as Ca2+ and Mg2+ will influence the rate and extent of aggregation/agglomeration (e.g. a higher ionic LCA of Nanomaterials


Fate ENMs/NMs Manufactured Natural Incidental Point Manufacturing, landfills, wastewater effluent etc. Non-point Wear of tyres, sunscreen, brake pads etc. Emission Water Soil Air ENMs/NMs ENMs/NMs ENMs/NMs Surfactant stabilised AggregaƟon/ agglomeraƟon Bio-degradaƟon, photolysis, hydrolysis Binding to NOM & other colloids SedimentaƟon DissoluƟon Binding to suspended parƟcles/ biota ParƟcle migraƟon DissoluƟon Biota Human InhalaƟon IngesƟon Dermal Plant uptake & bioaccumulaƟon GravitaƟonal setling Wet & dry deposiƟon Difussion AggregaƟon/ agglomeraƟon DeposiƟon Invertabrate uptake & interacƟons Microbial interacƟons SorpƟon AggregaƟon/ agglomeraƟon OxidaƟon Impact species Fig. 32.4 Potential environmental pathways of released engineered and naturally occurring nanomaterials (ENMs and NMs) in air, water, and soil and related to common impact organisms (Miseljic and Olsen 2014) M. Miseljic and S.I. Olsen


strength will lead to more aggregation/agglomeration, as is the case for marine waters compared to freshwater). Brant et al. (2005) exemplify that if the ENM C60 fullerene is released into water with an ionic strength higher than 0.001 M the formed aggregates/agglomerates will sorb to other particles and media and eventually become immobilised. This happens in particular when pH is close to the isoelectric point (i.e. pH at which a molecule has no net electrical charge), since the particle charge is then lower and a change in repulsive forces is able to promote aggregation/agglomeration (Franklin et al. 2007; Illés and Tombácz 2006). In water, the gravitational forces cause the aggregates/agglomerates to sediment, thus becoming less available to certain aquatic organisms, but more to the benthic organisms (Klaine et al. 2008; Lowry et al. 2012)


2008; Lowry et al. 2012). Subsequently, turbulent motion in benthos and bio-turbulation in the sediment can cause the ENM to be re-suspended and become more available again in the water phase. 32.4.4 Important Physico-Chemical Characteristics Considering the developing understanding of the fate of ENMs in freshwater, the following physico-chemical ENM properties are important to consider (Batley and McLaughlin 2010; Klaine et al. 2008): • Chemical composition • Mass • Particle number and concentration • Surface area concentration • Size distribution • Specific surface area • Surface charge/zeta potential • Surface contamination and the nature of any shell and capping • Solubility • Crystal structure. In addition to the ENM specific properties, the natural conditions of the surrounding environment are also important when dealing with ENM fate


In addition to the ENM specific properties, the natural conditions of the surrounding environment are also important when dealing with ENM fate. However, aggregation/agglomeration and dissolution alongside other co-related mechanisms are neither fully understood nor well represented with characterisation data, in relation to mechanisms shown in Fig. 32.4 (Farré et al. 2011). 32.4.5 Toxicity The toxic effects from ENPs and ENMs depend on several parameters, e.g. size, surface/crystal structure, dissolution and aggregation/agglomeration. Size of ENPs is proven to have an influence on the level of toxicity, e.g. 48 h testing on Daphnia LCA of Nanomaterials


magna showed, according to Zhu et al. (2009), a 143 mg/L LC50 for <25 nm TiO2 (20% rutile and 80% anatase crystal structure), while Heinlaan et al. (2008) observed a 20,000 mg/L LC50 for 25–75 nm TiO2 (crystal structure not reported). These two studies along with several others (e.g. Kashiwada 2006; Hussain et al. 2009) indicate large variations between different nominal sizes of ENPs, but it needs to be underlined that tests are difficult to compare due to variations in test conditions. In addition to size, crystal structure also has an influence on toxicity, where, e.g. TiO2 in anatase crystal structure is known to be more toxic to organisms than in rutile form. Toxicity is dependent on the intrinsic toxicity potential of the ENMs, influenced by, e.g. size, and ions formed through oxidative dissolution (Scheringer et al. 2010)


Toxicity is dependent on the intrinsic toxicity potential of the ENMs, influenced by, e.g. size, and ions formed through oxidative dissolution (Scheringer et al. 2010). The high toxicity potential of free Ag ions in natural waters may be disrupted by the presence of complexing ligands, as they reduce the Ag ion concentration and thus the bioavailability (Scheringer et al. 2010). In addition, toxicological effects also depend on the surface structure of ENPs, where surface structure can be removed/altered, e.g. by natural and anthropogenic chemicals in the environment. Change in surface structure may result in enhanced mobility, bioavailability, aggregation (mainly hydrophobic surfaces), sedimentation, dissolution and dispersion (mainly hydrophilic surfaces), and consequently the actual exposure and toxicity may increase (Vonk et al. 2009; Lowry and Casman 2009; Ratte 1999)


2009; Lowry and Casman 2009; Ratte 1999). Further, the pH and presence of adsorbing molecules and ions have an influence on ENM fate and eventually the toxicity. The correlation of various mechanisms in ENM behaviour and impacts, in contrast to single-chemical behaviour, means that single-chemical impact models are not suitable to be applied for ENMs (Lowry and Casman 2009). 32.5 Conclusion: What to Consider When Performing LCA on ENMs Currently, LCA of ENMs is deficient in several areas. First, novelty of the nano-technology is limiting the availability of LCI data. Second, the potential release of ENMs/ENPs is difficult to include in LCA at this point. This is due to both lack of LCI data and an incomplete understanding of fate, exposure and effects (eco- and human toxicity characterisation factors). Figure 32.5 illustrates the current state of LCA for nano-technological products


Figure 32.5 illustrates the current state of LCA for nano-technological products. Based on already performed LCAs on ENMs there should be awareness of the following when aiming at performing an LCA on ENMs: • Goal and scope: If possible consider the whole life cycle and be realistic when setting goals (see Fig. 32.5). The functional unit needs to take into account the potential differences in functionality of the product when using ENMs. • LCI: Data is difficult to acquire, so either collaborate with the industry or base the data on already published studies and generic processes from databases as M. Miseljic and S.I. Olsen


e.g. ecoinvent. Be aware that lab-scale production data can be misleading and need to be scaled up. • LCIA: A completely holistic impact assessment cannot yet be performed, mainly due to the challenges related to quantification of ENM/ENP release and the related impacts (Som et al. 2010; Bauer et al. 2008): – How much ENM/ENP is released to environmental compartments (e.g. water) and technosphere (e.g. waste water treatment)? – Which exposures to ENM/ENP occur in the environment and what are the effects on biota and humans? – At different times, what appearance (size, shape and composition) do the ENMs/ENPs take in the environment (primary particles (ENPs), agglomerated ENPs, aggregated ENPs, agglomerated aggregates)? – What are the environmental consequences from different end-of-life treatment of ENM products? ExtracƟon of raw materials - Generic processes Manufacturing - Inventory difficulƟes Use - Product dependent Disposal - Unknown conseq


“Cradle-to-gate” “-grave” Primary parƟcle (ENP) Agglomerate d ENPs Aggregate d ENPs Agglomerated aggregates Resources Resources Resources Resources CO2 CO CH4 NOx SO2 NMVOC NH3 N2O Waste etc. Life cycle stages Environmental compartments Impact categories Water, soil and air Water, soil and air Global warming AcidificaƟon EutrophicaƟon Ozone depleƟon Photochemical ozone formaƟon Human toxicity Ecosystems toxicity Resource depleƟon Waste Etc. Ecosystems & human toxicity Possible in LCA Currently not possible in LCA Fig. 32.5 Possibilities and limitations of LCA: what currently can be assessed in LCA (Miseljic and Olsen 2014). Full lines illustrate what is currently done (and is possible at the stage of current research) while dotted lines illustrate the challenges, especially related to the assessment of release of nanoparticles LCA of Nanomaterials


So far, the LCAs performed on ENMs have been used to assess the accountable production-related emissions. Future LCAs should seek to develop the areas that are currently poorly covered, so that impact burden shifting is avoided. This may be done by including other approaches (e.g. RA) using LCA as a framework for gathering the best developed approaches in order to perform a holistic environmental impact assessment. Aitken, R.A., Bassan, A., Friedrichs, S., et al.: Specific Advice on Exposure Assessment and Hazard/Risk Characterisation for Nanomaterials Under REACH (RIP-oN 3). Final Project Report, RNC/RIP-oN3/FPR/1/FINAL. http://ec.europa.eu/environment/chemicals/nanotech/ pdf/report_ripon3.pdf (2011). Accessed 9 Nov 2011 Babaizadeh, H., Hassan, M.: Life cycle assessment of nano-sized titanium dioxide coating on residential windows. Constr. Build. Mater. 40(March), 314–321 (2012) Batley, E.G., McLaughlin, J.M.: Fate of Manufactured Nanomaterials in the Australian Environment