Waste Management 38 (2015) 474–485

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Life cycle assessment and residue leaching: The importance of parameter, scenario and leaching data selection E. Allegrini a,⇑, S. Butera a, D.S. Kosson b, A. Van Zomeren c, H.A. Van der Sloot d, T.F. Astrup a a

Technical University of Denmark, Department of Environmental Engineering, Building 115, 2800 Lyngby, Denmark Vanderbilt University, Department of Civil and Environmental Engineering, Box 1831 Station B, Nashville, TN 37235, USA c Energy Research Centre of the Netherlands (ECN), Department of Environmental Risk Assessment, P.O. Box 1, 1755 ZG Petten, The Netherlands d Hans van der Sloot Consultancy, Dorpsstraat 216, 1721 BV Langedijk, Netherlands b

a r t i c l e

i n f o

Article history: Received 9 September 2014 Accepted 16 December 2014 Available online 5 January 2015 Keywords: Leaching Residues LCA MSWI Bottom ash Toxic impact

a b s t r a c t Residues from industrial processes and waste management systems (WMSs) have been increasingly reutilised, leading to landfilling rate reductions and the optimisation of mineral resource utilisation in society. Life cycle assessment (LCA) is a holistic methodology allowing for the analysis of systems and products and can be applied to waste management systems to identify environmental benefits and critical aspects thereof. From an LCA perspective, residue utilisation provides benefits such as avoiding the production and depletion of primary materials, but it can lead to environmental burdens, due to the potential leaching of toxic substances. In waste LCA studies where residue utilisation is included, leaching has generally been neglected. In this study, municipal solid waste incineration bottom ash (MSWI BA) was used as a case study into three LCA scenarios having different system boundaries. The importance of data quality and parameter selection in the overall LCA results was evaluated, and an innovative method to assess metal transport into the environment was applied, in order to determine emissions to the soil and water compartments for use in an LCA. It was found that toxic impacts as a result of leaching were dominant in systems including only MSWI BA utilisation, while leaching appeared negligible in larger scenarios including the entire waste system. However, leaching could not be disregarded a priori, due to large uncertainties characterising other activities in the scenario (e.g. electricity production). Based on the analysis of relevant parameters relative to leaching, and on general results of the study, recommendations are provided regarding the use of leaching data in LCA studies. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Residual products from industrial processes are being regarded increasingly for their potential as a valuable resource, and utilisation rates have increased in the last few decades (e.g.

Abbreviations: BA, bottom ash; CF, characterisation factor; CPR, construction products regulation; CTUe, comparative toxic unit for ecotoxicity; CTUh, comparative toxic unit for human toxicity; DOC, dissolved organic carbon; ET, ecotoxicity; FU, functional unit; HTc, carcinogenic human toxicity; HTnc, non carcinogenic human toxicity; LCA, life cycle assessment; LCI, life cycle inventory; LCIA, life cycle impact assessment; L/S, liquid-to-solid ratio; MSW, municipal solid waste; MSWI, municipal solid waste incineration; TC, transfer coefficient; WMS, waste management system; WtE, waste-to-energy. ⇑ Corresponding author at: Technical University of Denmark, Department of Environmental, Engineering, Building 115, 2800 Lyngby, Denmark. Tel.: +45 45251572. E-mail addresses: [email protected], [email protected] (E. Allegrini). http://dx.doi.org/10.1016/j.wasman.2014.12.018 0956-053X/Ó 2014 Elsevier Ltd. All rights reserved.

Miljøstyrelsen, 2012). Metallurgical slags, residues from power plants, biomass and waste incineration residues and construction and demolition waste are examples of residual products characterised by a resource potential. Residual materials can be used as secondary sources of metals (e.g. scrap metal, metal-bearing minerals), nutrients (e.g. phosphorus) and aggregates or other construction materials (e.g. additives in cement). While utilisation as aggregates or additive materials in concrete and cement production is limited to a few types of residues (e.g. coal fly ash, blast furnace slag), the utilisation of residual products as unbound materials for road construction has been applied extensively in several countries (Hendriks and Janssen, 2001; Hansen and Lauritzen, 2004; Apul et al., 2007; Astrup, 2007; Thøgersen et al., 2013). Although compliance with technical requirements is the primary criterion for utilising such materials, environmental regulations have also been developed to assess the environmental compatibility of re-use practices.

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Many studies have investigated the release of pollutants (i.e. leaching behaviour) from residual materials. Experimental procedures have been developed to quantify leaching and efforts focused on the harmonisation of leaching procedures (Kosson et al., 2002; Van der Sloot et al., 2003; Van der Sloot and Kosson, 2012) including current US and international efforts towards a consistent basis using the Leaching Environmental Assessment Framework (Kosson et al., 2014; USEPA, 2010, 2012a,b). Leaching testing is used as a compliance procedure in legislation addressing the utilisation of materials in construction works (e.g. Building Materials Decree, 1995; Soil Quality Decree, 2007; N.1662:2010), as a basis for unrestricted use of coal fly ash as a supplemental cementitious material in concrete (EPA, 2014) as well as landfill acceptance criteria (EC/33/2003), and limit values have been defined for inorganic compounds based on risk assessment approaches (Hjelmar, 2003). The release of toxic pollutants into the environment, as a result of the utilisation or disposal of residual materials, has also been addressed through life cycle assessments (LCAs) (e.g. Finnveden et al., 1995; Hellweg, 2000; Doka and Hischier, 2005; Birgisdóttir et al., 2007; Carpenter et al., 2007, 2013, Obersteiner et al., 2007; Doka, 2009; Laner, 2009; Manfredi and Christensen, 2009). However, the uneven availability and quality of data for individual activities included in the LCA system, and significant uncertainties regarding toxic impact characterisation (Hauschild et al., 2013), have often prevented the inclusion of toxic impact categories in existing LCA studies (e.g. Mroueh et al., 2001; Mercante et al., 2012); for example, toxicity indicators are not included in the European standards (EN 15804:2012; EN 15978:2012) dealing with LCAs in the context of the Construction Products Regulation (CPR) (Regulation No. 305/2011). In addition, the time horizon represents a crucial factor, since leaching frequently is believed to occur over a very long time frame (i.e., greater than 100 years), albeit often at very low concentrations. However, the significant uncertainty related to leaching predictions over long time frames, and the methodological/ethical aspects related to accounting for emissions occurring in the far future (e.g. cut-off rules, discounting of future emissions), have led most authors to distinguish between short-term emissions (typically 100 years in LCA studies) and longterm emissions (after 100 years). In some studies, specific impact categories have been used to account for the potential long-term emissions, for example ‘‘stored ecotoxicity’’ in relation to soil and to water (e.g. Christensen et al., 2007; Turconi et al., 2011). However, no general methodology has been identified as the preferred way to include experimental leaching data in an LCA, and various approaches are therefore being applied currently (cf. Bakas et al., 2013). In other utilisation scenarios, i.e. road base applications, the time horizon may be less critical compared to landfill disposal, as the lifespan of the road structure is limited and this application does not represent a permanent disposal for the residues. LCA studies that assess leaching mainly apply a generic approach to convert the liquid to solid ratio (L/S) of a leaching test to a projected time frame, by employing basic assumptions in relation to the application scenario (e.g. Kosson et al., 1996). Birgisdóttir (2005) developed an LCA model dedicated to road structures, with and without municipal solid waste incineration bottom ash (MSWI BA) included as an aggregate. Emission data in the LCA accounted for the cumulative release extrapolated at the relevant L/S based on two-stage batch leaching tests (i.e. EN 12457-3). Toller (2008) and Mroueh et al. (2001) applied the result of simple batch leaching tests at L/S 2 or 10 to include leaching from road construction with MSWI BA. Data for leaching as a function of pH were included in some LCA studies, such as Mroueh et al. (2001). The partitioning of leaching emissions between soil and water compartments after assumed release to the environment has been addressed by a few studies: Hellweg et al. (2005), for instance, pro-

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posed an advanced approach using a step procedure to determine soil characteristics beneath a landfill and the subsequent interaction between soil and metals, while Birgisdóttir (2005) defined fixed transfer coefficients (TC) based on conservative assumptions and Schwab et al. (2014) estimated TC by means of soil water partitioning coefficients (Kd) based on soil and element characteristics. Despite the various literature studies addressing LCA of waste management systems (WMSs) and impacts from leaching emissions due to residue disposal or utilisation, the significance of leaching emissions in these contexts is still unclear and poorly described. Moreover, the importance of leaching data selection for the LCA results is not well documented, e.g. data from various laboratory leaching procedures may be used or data may simply be based on assumptions. In this context, the aim of this study is to provide a systematic overview of the importance of including leaching in relation to residue management within LCA studies of WMSs. The specific objectives are to: (i) define the source term and compare pollutant release by leaching, estimated based on key testing procedures; (ii) illustrate the effect of leaching data in an LCA by applying the release data to three selected LCA case scenarios with increasingly large system boundaries; (iii) evaluate uncertainty in the LCI of background activities and the importance of framework conditions; (iv) assess the importance of data quality and coverage; (v) propose an alternative method to assess contaminant transport into the environment and enhance the determination of transfer coefficients into soil and water compartments for use in an LCA and (vi) identify critical aspects and provide recommendations for LCA practitioners on how to address leaching emissions from residual materials in construction.

2. Methodology 2.1. Material and data MSWI BA was chosen as a case material. This choice was based on: (i) the large amounts of this material available, as waste incineration has gained increasing importance in modern WMSs, (ii) the availability of data and characterisation studies in the scientific literature and (iii) the existence of large databases with leaching data from waste to energy (WtE) plants, due to compliance testing requirements. Current disposal/utilisation options for MSWI BA are: landfilling in dedicated compartments of a landfill, utilisation as a construction material within the landfill and utilisation as aggregate in construction works (e.g. roads, embankments). Leaching data in this study were based on MSWI BA produced at various incinerators in Denmark (six incinerators). The dataset included: 60 batch leaching tests at L/S = 2 l kg1 (EN 12457-1), performed for compliance purposes over a two-year period (2010–2011), seven two-step leaching tests providing cumulative releases at L/S = 10 l kg1 (EN 12457-3), performed for compliance purposes over a two-year period (2006–2007), and five column leaching tests from L/S 0.1 to 10 l kg1 (prEN 14405), carried out for research purposes and published by Astrup et al. (2010). Leaching data were compared by means of a one-way analysis of variance and a pairwise t-test with Bonferroni correction implemented in R v.6.0 software (www.rstudio.com). The effects of pH on leaching were evaluated based on pH-dependent leaching data (EN 14429 and EN 14997) taken from the large LeachXS leaching framework database, including data on Dutch MSWI BA (van der Sloot et al., 2008a). The toxic substances taken into consideration were inorganic elements included in the USEtox model – a recommended life cycle assessment impact (LCIA) model for characterising the human- and eco-toxicological impacts of chemicals (Rosenbaum et al., 2008) –

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such as As, Ba, Cd, Cr, Cu, Mo, Ni, Pb, Sb, Se, Sn, V and Zn. Due to a lack of data or values consistently below detection limits, Ag, Be, Co, Hg and Tl were excluded. 2.2. Utilisation scenario In this study, MSWI BA was utilised as an unbound aggregate in a road sub-base in Denmark. Road structure parameters were retrieved from literature studies (e.g. Birgisdóttir, 2005; Hjelmar et al., 2007) and guidelines drawn up by local authorities (e.g. Danish Road Directorate, 2012). The percolation approach was used to define the contact between material and water (L/S) over 100 years, and the expected L/S under field conditions was calculated according to Kosson et al. (1996): 1

L=S½l  kg  ¼

I½%  T½years  P½mm  year1  q½kg  m3   h½m

ð1Þ

All parameters – except time (T), which was fixed at 100 years – were defined by a range and a probability distribution based on literature data and the authors’ expertise. Precipitation (P) was based on annual Danish precipitation (DMI, 2014), which was assumed normally distributed within the range of 500 and 900 mmyear1. Infiltration values (I) were deduced based on various studies (e.g. Ramier et al., 2004; Taylor, 2004; Hjelmar et al., 2007; Dabo et al., 2009; Dawson, 2009; Ramier et al., 2011) and on technical recommendations made by local authorities (i.e. N.1662:2010): infiltration values were assumed to be log-normally distributed by 7% on average and with a geometric standard deviation of 2. BA density (q) in the road sub-base was assumed to vary between 1500 and 2100 kg m3 as reported in Hjelmar et al. (2010) and in the guidelines provided by the Danish Road Directorate (2012), and in the absence of additional information, density values were assumed uniformly distributed. Furthermore, the thickness of the BA layer (h) was considered as uniformly distributed between 0.2 and 1 m. The thickness of the BA layer is generally around 0.35 m; however, the range of thickness was extended up to 1 m, in order to include its utilisation in low embankments. The abovementioned parameters and their probability distributions were used through Monte Carlo simulations to calculate the most probable cumulative L/S in a specific situation for the selected 100 year time interval. Additional information is reported in Appendix A. 2.3. Leaching data elaboration Batch test data were used for the cumulative release at L/S = 2 and 10 l kg1 (data B_2 and B_10). Column test data as reported by Astrup et al. (2010) were used to estimate the cumulative release at L/S = 2 and 10 l kg1 (data C_2 and C_10), and, through interpolation (i.e. by means of least squares fitting using either a power law or logarithmic function), the cumulative release at an L/S larger or smaller than those achieved during the experimental procedure: cumulative release at the most probable L/S ratio (50th percentile) and at the extremes of the 90% confidence interval (5th and 95th percentile) were calculated. Leaching data were labelled with a letter and a number, indicating the experimental procedure (B: batch; C: column) and the L/S, respectively. All experimental and extrapolated column leaching data (i.e. five set of data) were used to represent the contaminant leaching at the estimated 5th, 50th and 95th percentile L/S. Additional leaching data were used through sensitivity analysis to obtain information concerning pollutant release behaviour and speciation, which are important for estimating contaminant transport into environmental compartments (i.e. soil and water) and contaminant toxicity. For this purpose, leaching data based on pH-dependent tests were used. Data from an extensive Dutch data-

base on MSWI BA were used as inputs into geochemical modelling, with the Orchestra chemical speciation and transport model embedded in LeachXS (Van der Sloot et al., 2008b). Details on the geochemical modelling of the leaching data, including discussion of the uncertainties associated with the geochemical modelling, and on the pH-dependent leaching data are reported in Appendix B. 2.4. LCA 2.4.1. Scope and scenario definition Three individual LCAs were performed, by following an attributional approach (ILCD, 2010), as the purpose of the LCAs was to describe the importance of the impacts related to leaching emissions in relation to the impact of existing LCA scenarios, rather than analysing impacts caused by a change with respect to a reference scenario. Following the attributional approach and common LCA practices, average data were used and allocation problems were avoided by applying system expansion (cf. ILCD, 2010). In each LCA a different scenario was analysed. Scenarios in the three LCAs were concentric, meaning that each scenario was built by expanding the system boundaries of the previous one. This was done to illustrate the importance of leaching in the context of different LCA scenarios typically applied within waste LCAs. Fig. 1 schematises the three scenarios, while detailed information about input data and the processes chosen is given in Appendix C. The functional units (FU) of the three scenarios were:  Scenario 1, ‘‘management of 1 Mg of MSWI BA in road subbases in Denmark’’.  Scenario 2, ‘‘management of 1 Mg of MSWI BA in road subbases in Denmark, including the prior incineration process of residual household waste’’.  Scenario 3, ‘‘management of 1 Mg of treated BA in road subbases in Denmark, including the prior WMS in which paper and glass are source-separated and residual household waste is incinerated’’. LCAs were performed with the LCA software SimaPro v.8.0.2, which includes the life cycle inventory (LCI) database Ecoinvent v.2. 2.4.2. LCIA Emissions into the environment were characterised using the USEtox life cycle impact assessment (LCIA) methodology (Rosenbaum et al., 2008), as recommended by the UNEP-SETAC Life Cycle Initiative and the Joint Research Centre (JRC) of the European Commission (ILCD, 2011; Hauschild et al., 2013). However, the characterisation of inorganic elements is classified as ‘‘interim’’ and is thus to be used with caution, because of the large amount of uncertainties in the characterisation factors (CFs). The impact categories included were carcinogenic and non-carcinogenic human toxicity (HTc and HTnc) and ecotoxicity to freshwater (ET). Leaching emissions were inventoried as cumulative contaminant release into ‘‘industrial’’ soil, thus setting the boundaries between the natural environment and the technosphere below the BA layer in a road; the subgrade (native soil layer underneath construction work) was included in the ‘‘natural environment.’’ Using this approach, the partitioning of pollutant emissions into the natural environment was determined wholly by the fate modelling included in the USEtox model (Rosenbaum et al., 2011; Henderson et al., 2011), which is based on inter-media transport and removal processes on a continental and global scale, using physical–chemical characteristics of a substance combined with a default landscape (including geometric and climate parameters affecting the residence time of a substance in an environmental

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Scenarios and system boundaries

Metals production

Avoided primary production

Material recycling MSW

Residual MSW

WtE

Electricity generation

1

Metals recycling

Gravel production

BA treatment

BA in road

Heat production

2

3

Fig. 1. Schematic overview of the scenarios.

compartment). However, such an approach may involve certain critical aspects, namely: (i) the subgrade could be engineered soil modified for compliance with structural requirements, so the soil could be included in the technosphere, and (ii) within the 100-year time horizon used for the inventory, released pollutants migrate into the soil and could reach the shallow groundwater according to specific geometric, and local climate parameters (instead of global), and to interactions between substances and soil which are not included in the default USEtox fate modelling. Thus, in sensitivity analysis, a second approach was implemented and in this study a state-of-the-art approach was used to calculate transfer coefficients (TCs) to soil and groundwater using the Orchestra geochemical modelling framework (Meeussen, 2003) and other developed sub-models (Dijkstra et al., 2004, 2009) that have been applied in the Netherlands to derive environmental criteria for the re-use of materials (Verschoor et al., 2006). In order to do so: leaching emissions from five column leaching tests (see 2.1.) were input into LeachXS; the reactive transport of contaminants through a 0.95 m-deep layer of soil (underneath the road structure) was modelled based on multisurface models as implemented in Orchestra and described in Dijkstra et al. (2004 and 2008); reactive transport was modelled identically to the developed model used to derive limit values in the Dutch Soil Quality Decree using three soil types (clay, peat and sand), as defined in Verschoor et al. (2006), and soil solution concentrations of contaminants at 0.95 m depth over 100 years were calculated and integrated to define the cumulative release of contaminants into the liquid phase at that depth. The release of contaminants into the liquid phase at 0.95 m depth, as determined by this approach, was compared with the total release from BA into the subgrade over 100 years, following which TCs between the liquid phase and the soil in the subgrade were estimated. Pollutants found in the liquid phase at 0.95 m depth were assumed also to be emissions into freshwater, as it was assumed that pollutants would end up in shallow groundwater recharging surface water bodies. In addition, two opposite assumptions were tested for pollutants retained in the subgrade soil: (i) the soil in the subgrade was regarded as natural environment (as in the base scenario); (ii) system boundaries of the technosphere were expanded to include the subgrade, thus released pollutants remaining in the subgrade layer were not contributing to any toxic impact. Additional information is reported in Appendix D. In both

cases, defaults CFs from USEtox were applied to emissions into water and soil. Thus, overlapping between the calculated TC and the fate modelling within USEtox occurs within the 100-year time frame. However, the calculated TC belong to the inventory phase, describing the emission occurring in 100 years, while fate modelling within USEtox belongs to the characterisation of the emission and describes the partitioning of the emission between environmental compartments based on equilibrium between well-mixed compartments – and thus virtually independently of the time frame.

3. Results and discussion 3.1. Source term The average L/S at the utilisation scenario was 7.0 l kg1 after 100 years, with a standard deviation of 7.2 l kg1. However, the distribution of the L/S values appeared clearly skewed to the left following a lognormal distribution, and so data from column tests were inter- or extrapolated at L/S ratios corresponding to the 5th, the 50th and the 95th percentiles of the L/S distribution at 100 years resulting from the Monte Carlo analysis. The resulting values were 1.3, 4.9, and 20 l kg1, respectively (leaching data C_1.3, C_4.9 and C_20). The range found was quite large and resulted from several assumptions; however, it was found appropriately wide enough for the exemplificative scope of this study. Cumulative release data, as derived from batch and column leaching test procedures at various L/S ratios, are reported in Fig. 2. Although differences in the release were element-specific and reflected the detailed geochemical conditions in the leaching test systems, for the purpose of the LCA the leaching data for inorganic substances could be classified roughly into two groups: (i) data mainly affected by L/S and (ii) data mainly affected by the test procedure (the batch or column test). Ba, Pb (to a lesser extent), Sb, Zn and V belonged to the first group, in agreement with their solubility controlled release: increasing L/S caused increased dissolution of the mineral phase controlling the release of the element of interest, thus resulting in a higher cumulative release. As, Cd, Cu, Ni and Se were mainly affected by the choice of leaching test. Particularly for Cu, this is in accordance with its solubility, but also with

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Fig. 2. Cumulative metal release as estimated by different leaching tests and L/S ratios. Release data are reported in mg kg1 and datasets are shown in the form of standard boxplots: horizontal lines of the boxes represent the 25th, 50th and 75th percentiles of the distribution, while the whiskers extend to the farthest observation. Data farther than 1.5 times the distance between 25th and 75th percentile (box size) from the median are represented with single points. Values below the detection limit were reported and used as half of the detection limit. Data are reported on a logarithmic scale.

the change in dissolved organic matter (DOC) – which complexates Cu and increases its solubility – due to the change in the type of test. Release data based on batch tests (B_2 and B_10) were generally found to be higher than those from column tests at the same L/ S: Cd release was above detection limits only in eluates from batch tests, while As, Cu, Ni and Ba (Ba only for L/S 2) release were higher in batch tests, as shown similarly in previous studies (e.g. Lopez Meza et al., 2008). Se and Zn behaved conversely, as also reported by Hage and Mulder (2004) and Quaghebeur et al. (2006) on other types of residues, while Cr, Mo and Sn did not present significant differences in releases across test methods and L/S values. However, these conclusions are limited by the lack of data for some of the elements in the batch tests (e.g. Sb, Mo, Se, Ba, Sn and V were not measured in the leachate from EN 12457-3). Nonetheless, the relatively large difference between detection limits in the datasets for batch tests and column tests (ranging from a factor 2–20) do not allow for excluding that such differences between batch and column data were artefacts rather than reality. Fig. 2 shows that the variability of contaminant release data for some of the elements was up to two orders of magnitude (see also Fig. B.1 in the supplementary material). Particularly, leaching data from the batch leaching test at L/S 2 (B_2) were characterised by significant variability, not only because of the variability of the sample materials analysed during a two-year period but also because of artefacts related to the batch procedure itself (e.g. Grathwohl and Susset, 2009; Krüger et al., 2012; USEPA, 2012a). Although pollutant release is consistently determined by a defined

leaching mechanism (i.e. solubility, availability, sorption, complexation), the range of release data variations extends over a few orders of magnitude. This is caused mainly by heterogeneity of the material relative to waste input, the incineration process and the extent to which the processes/surfaces that control leaching are present in the material. Hence, experimental data variability should be acknowledged when assessing the impact of a residual material.

3.2. Impact of leaching in LCA scenarios Fig. 3a reports LCA results for scenarios 1, 2 and 3 (excluding leaching data) and separately (labels C_1.3 to C_20) for impacts related to the leaching of pollutants from MSWI BA, calculated using the cumulative release discussed above (a table with the LCA results is reported in Appendix E). Results are expressed as human and eco-toxicity potentials through comparative toxic unit (CTU) in relation to the FU (cf. 2.4.1.): CTUh for HTc and HTnc, CTUe for ET. In each chart results are reported together, with error bars representing the 90% confidence interval. Based on Fig. 3a leaching emissions from MSWI BA appeared to have little importance in systems larger than simple road construction and related material transport (i.e. systems larger than scenario 1). Leaching impacts were more than two orders of magnitude smaller than total impacts from scenarios 2 and 3 (comparing median values). However, in the case of ET, C_20 impact was up to the same order of

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Fig. 3. (a) Results of the LCA analyses of scenarios 1, 2 and 3 (first three bars in each chart), excluding impacts as a result of the leaching of metals from MSWI BA, which are reported separately in the next bars. Each bar represents the 50th percentile of the characterised LCA result, while error bars represent the range of variation of the results between the 5th and 95th percentiles. (b) Detailed results for scenario 1 and leaching emissions reported on a logarithmic scale.

magnitude as scenario 3, reaching 35% of the net impact of scenario 3. Average impacts for scenarios 2 and 3 were negative for all impact categories, mainly due to savings related to avoided electricity and heat production, while scenario 1 and all impacts as a result of leaching emissions from BA were expectedly positive (burdens to the environment). The incineration process played the biggest role in determining the final result of the LCA in scenarios 2 and 3. The results of scenarios 2 and 3 presented an extremely wide range of variations (e.g. from 0.00097 CTUh to +0.00015 CTUh for HTnc in scenario 3) owing to uncertainties in the inventory data which were accounted for through MonteCarlo analysis as implemented in SimaPro. Leaching of pollutants from MSWI BA caused relevant impacts with respect to scenario 1 (see Fig. 3b), with impacts at least one order of magnitude greater than scenario 1. Focusing only on leaching impacts, significant differences between impact scores as a result of different cumulative pollutant releases were observed for HTnc and ET, while HTc was not sensitive to changes in the estimated L/S or the leaching test procedure. For HTnc significant differences were found between leaching emissions at around L/S 2 (C_1.3–C_2) and leaching data above L/S 4.9. Moreover, impacts due to the leaching of contaminants, as estimated at L/S 20 l kg1 (the release at the highest expected L/S value), provided LCA results significantly different from other L/Ss in both HTnc and ET. The effect of the leaching test procedure appeared not to have an effect on the final results of the two L/S ratios investigated (L/S 2 and 10 l kg1), implying that both batch tests and column test data can be used for LCA. Obviously, the choice of a specific L/S ratio had an effect on the results, since generally more contaminants are released (in mg/kg) at higher L/S ratios. Examples of these typically solubility-controlled elements are Sb, V and Zn, which at the same time showed the highest CFs in the USEtox methodology. However, after testing an additional leaching dataset at various L/S ratios (L/S

values of 4, 6, 7, 8, 9, 11, 12, 13, 14, 15 and 22 l kg1, results not reported), it was observed that HTnc and ET were not sensitive to L/S variations within the ranges of ±5 and ±10 l kg1, respectively (excluding values below 1.3 l kg1 and above 22 l kg1, which were not assessed). This observation implies that it is important to estimate the L/S ratio in the application scenario with an accuracy of about 5 l kg1 between values greater than 1.3 and less than 22 l kg1. 3.3. Critical pollutants and the importance of data coverage Fig. 4 reports the contribution of individual elements to total toxicity due to leaching. Cr (which was assumed to be entirely Cr(VI) (cf. Cornelis et al., 2008)) dominated HTc impacts, regardless of the leaching test and L/S adopted. Zn and As (assumed as As(V)) were the main contaminants impacting HTnc, and the contribution of Zn to the impact increased proportionally with the assumed L/S ratio. Cu, Zn and Sb (considered as Sb(V), based on Cornelis et al. (2008)) were the substances mainly responsible for ET impacts. Thus, most critical elements for the investigated materials in the utilisation scenario and for the LCIA methodology applied were As, Cr, Cu, Sb and Zn. Another aspect to be considered was data coverage. In the dataset used in this study, Sn and V were not measured in the batch tests at L/S 2 and 10 (B_2 and B_10), and additionally Ba, Mo, Sb and Se were not measured in batch tests at L/S 10 (B_10). Such asymmetry was related to compliance requirements, since the majority of the batch data used in this study were produced for regulatory purposes. With the current data, comparable potential toxicities were obtained for B_2 and C_2, and between B_10 and C_10 in all impact categories. However, Sb, and V to a lesser extent, was found to make a non-negligible contribution to HTnc and ET scores (see Fig. 4), with their sum contributing to an impact up to 7 and 26%, respectively, on each impact category. Therefore,

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C_20

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Sb Zn

40%

As

20%

Cr Cu

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0%

Fig. 4. Contribution of leaching of individual metals to toxic categories. Results are shown for the 50th percentile of the relative contribution of each element to the total impact of each set of leaching data. Elements contributing less than 2% to all impact categories and in all leaching datasets were included in the category ‘‘others.’’

the lack of data for V, and especially for Sb in B_2 and B_10, leads to a non-negligible underestimation of the impact on HTnc and ET. Thus, data coverage is a critical aspect which affects the quality and the results of the LCA. In larger systems with many activities included within the system boundaries, data coverage and consistency are crucial if one is to assess the system fairly. However, the use of secondary data in an LCA is often unavoidable, especially in relation to model background activities such as electricity production, which in turn increases any uncertainty around the measured emissions, due to inconsistent modelling and completeness. 3.4. Importance of framework conditions Concerning scenarios 2 and 3, sensitivity analyses were performed in relation to energy recovery and provision, which showed the most prominent relative impacts within each scenario. The average electricity mix was changed from the electricity mix of the Nordic Countries Association (NORDEL) to the European mix (RER), reflecting different assumptions with regard to location, fuel mixes and related parameters. As shown in Fig. 5a, the net results for scenarios 2 and 3 for HTc and HTnc showed approximately five times larger benefits by changing the electricity mix. In the second sensitivity analysis (see Fig. 5b) the energy recovery of the incinerator was modified from typical Danish values used in the base scenario (efficiency: 74% heat, 21% electricity) to a different case characterised by low recovery of heat because of the absence of a district heating network as reported by Turconi et al. (2011) for typical Southern European conditions (efficiency: 5.5% heat, 24% electricity). After changing energy recovery, all impact categories resulted in positive impacts in scenarios 2 and 3, and leaching data appeared important for ET, where impacts were of the same order of magnitude as scenario 2. These results were related strictly to the local production of electricity and district heating, and are typically characterised by large amounts of uncertainty which dominated the final output of the analysis. These processes are crucial

in LCA studies, as concluded by numerous studies (e.g. Fruergaard et al., 2009). An additional sensitivity analysis was done by excluding benefits from the substitution of energy in the incineration system. This was done to make a more robust comparison between leaching impacts and impacts from the rest of the systems, thereby minimising the effect of local conditions and modelling choices related to the energy system. Fig. 5b shows that leaching can be disregarded in systems without energy recovery when assessing HTc and HTnc, while it is not negligible for ET. Furthermore, ET impacts as a result of leaching ranged from one order of magnitude lower up to the same magnitude of the impact of scenarios 2 and 3 when the highest L/S was adopted. Two additional sensitivity analyses were carried out for scenario 1 (see Fig. 5c): (i) the impact of BA transportation was assessed by doubling the transportation distance and (ii) the process for producing gravel (primary material substituted by MSWI BA) was varied from a typical Danish gravel pit to an Ecoinvent process in which rock crushing was included. Although such changes affected in absolute terms the result of the LCA of scenario 1, the general conclusion of our study did not vary, since the leaching aspects remained of crucial importance to the final LCA result. 3.5. Impact of pollutant transport in soil, speciation and pH conditions Table 1 reports the results of calculations carried out with LeachXS–Orchestra, where reactive transport of the released pollutants was modelled by assuming three possible soils in the subgrade: sand, peat and clay. The majority of the TCs resulted as being 100% soil, meaning that based on the model calculations, most of the contaminant released from the BA remained in the subgrade soil (assumed to be approximately 1 m depth) after 100 years. Sb was the most mobile element, with 63% released into the water compartment. Similar conclusions have been drawn for Sb in previous studies (e.g. Scharff et al., 2011; Schwab et al., 2014). As shown in Fig. 6, by applying minimum TC to soil (see

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Fig. 5. Sensitivity analyses: (a) change in electricity mix; (b) change in heat and electricity recovery efficiency of the waste incinerator; exclusion of benefits from energy and heat substitution; (c) change of MSWI BA transportation distance; change of gravel production process.

Table 1 Transfer coefficients into soil: portion of released metal remaining in the one-metre soil subgrade after 100 years. Range of results based on reactive transport calculations in LeachXS–Orchestra, using five different column test leaching data as input and three soils: sand, peat and clay. The first two rows represent the overall minimum and maximum among the three soil types. Note, TC = 1 represents complete retention in the soil column. As

Ba

Cd

Cr

Cu

Mo

Ni

Pb

Sb

Se

Sn

V

Zn

Max Min

1 1

1 0.96

1 0.95

1 1

1 1

1 1

1 0.97

1 0.98

0.57 0.37

1 1

1 0.97

1 0.93

1 1

Sand Max Min

1 1

1 0.96

0.95 0.95

1 1

1 1

1 1

1 0.97

1 0.98

0.57 0.38

1 1

1 0.97

1 0.93

1 1

Peat Max Min

1 1

1 1

0.99 0.98

1 1

1 1

1 1

1 1

1 1

0.55 0.41

1 1

1 1

1 1

1 1

Clay Max Min

1 1

1 1

0.98 0.98

1 1

1 1

1 1

1 0.99

1 0.99

0.54 0.37

1 1

1 0.99

1 1

1 1

Table 1) instead of 100% emissions being directed to the soil compartment (as in the base scenario), the LCA results were not significantly affected, because of the small release of pollutants into water. However, if the subgrade was included in the technosphere, and thus no impact was associated with the contaminants retained in the first metre of soil, the final LCA result was significantly lowered (see Fig. 6). In that case, the leaching of contaminants from BA had an impact comparable with the impact of scenario 1 concerning HTc and HTnc, while for ET the impact from leaching was one or two orders of magnitude greater than scenario 1. This last case

reflected a scenario in which subgrade (contaminated soil) is also removed during road dismantling. Based on the results of the geochemical modelling – which are reported in Appendix B – and based on existing scientific literature (Dijkstra et al., 2006; van der Sloot et al., 2008a), two additional sensitivity analyses were included, in order to assess the impact of including the effect of the DOC complexation of metals (i.e. Cu and Zn) and the impact of varying the redox state of Cr. The results are reported in Fig. 6. Based on geochemical modelling, in the pH region of the used leaching dataset (between 8 and 12) for this type

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Fig. 6. Sensitivity analyses: direct emissions into the soil and water compartments; effect of including subgrade soil in the technosphere; effect of DOC complexation of Cu and Zn; effect of Cr redox state. On the right side in each chart the LCA results using pH-dependent data are reported as the 50th percentile and maximum and minimum value.

of material and application, on average 92% and 61% of the released Cu and Zn were DOC-bound, respectively. These results were in line with previous findings and could have important consequences from an ecotoxicity perspective. Metal toxic effect is related to metal bioavailability – and thus to the amount of free metal ions within the dissolved phase (Postma et al., 2009; Gandhi et al., 2010); in fact, many studies in the literature have analysed the effect of DOC complexation in lowering metal bioavailability and thus metal toxicity (e.g. Boyd et al., 2005; Steenbergen et al., 2005; Cooper et al., 2014,). In the sensitivity analyses this aspect was considered by multiplying the total Cu and Zn contribution to ET by a factor of 0.0805 and 0.39, respectively (see Appendix B for further explanations). ET impacts decreased on average by 66% with respect to the base scenario, but the general conclusions achieved with the base scenario remained unchanged. Concerning Cr, 80% of the total Cr released in the sensitivity analysis was assumed in the reduced form (Cr(III)) instead of 0% in the base case. This assumption was based on the geochemical modelling results, where Cr resulted on average 80% DOC-bound, thus as Cr(III). Organic matter and reducing inorganic substances in MSWI BA enhance reduction of Cr(VI) to Cr(III) which subsequently forms Cr(III)-DOC complexes. The impact of this modification was significant only on HTc, where the reduction accounted for, on average, 79% of the impact in the base scenario, not altering the main outcomes of the study, and thereby showing how the impact is completely and proportionally related to Cr(VI). However, careful consideration is also warranted regarding the domains in which geochemical modelling provides acceptable representation of leaching data and associated uncertainties. Because pH is one of the main parameters determining the release of inorganic substances from a given matrix, the influence of varying pH conditions was assessed. The set of release data used in this study was compared with pH-dependent data at L/S 10 l kg1 (see Fig. B.1 in Appendix B) and the release from batch and column tests at their natural pH and at various L/S were found to follow release curves as a function of pH. The data used (C_1.3–

C_20) had pH values ranging from 8.5 to 11.7, which are pH values typical of fresh to highly carbonated BA. pH-dependent release data in the pH range 7–8 were applied for sensitivity analysis, the results for which are reported in Fig. 6. The change in pH conditions towards neutral conditions did not affect the HTc. This observation was consistent with the solubility curve of Cr, which was approximately constant in the pH range 7–12. On the other hand, HTnc and ET were affected by the lower pH conditions, resulting in impacts approximately nine and five times higher than the impacts obtained with leaching data at the same L/S ratio (B_10 and C_10) for the original pH. This result was caused by a higher release of Cu, Zn and Sb at those pH conditions, which contributed 40, 29 and 27%, respectively, to ET. Zn had the highest contribution for HTnc (approximately 84%). Due to their solubility curves and high characterisation factors, Cu and Zn could affect LCA results significantly, so a correct assessment of the utilisation scenario in terms of pH and L/S conditions is of great importance. pH 7–8 represents highly carbonated BA, and it was assumed to be achieved within 100 years based on observations of full scale applications (e.g. Bendz et al., 2006, 2009). However, pH is expected to vary gradually over the 100 years, but this example is illustrative of the potential effects of changing pH on the LCA results. In addition, BA that is aged prior to use is anticipated to have a lower initial pH (typically closer to pH 10) than fresh BA. 3.6. Recommendations On the basis of the results obtained in this study, general recommendations can be made to LCA practitioners dealing with the leaching of contaminants from secondary materials used as construction material. Although every residual material has its own peculiarity (in terms of utilisation practices, leachability of pollutants, release mechanisms and conditions), some of the traits that emerged in this study based on MSWI BA can be extended to other materials. It was observed that the impacts of contaminant leaching from MSWI BA, regardless of the type of leaching data (i.e. test method),

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should not be a priori disregarded in LCA studies of WMSs where WtE systems or more in general benefits from energy recovery have a prominent role. Even though the leaching of pollutants from BA appeared to have negligible impacts with respect to emissions related to other processes in the WMS, the uncertainty in the LCI of each activity – and most importantly the site specificity of WtE efficiency and the benefits achievable through energy recovery – rendered the results non-robust and highly case-specific. The results showed that the abovementioned uncertainties can completely reverse the results (from positive to negative impacts), meaning that net small (even null) impacts are within the range of possibilities. Thus, in a conservative approach, leaching data should also be included in LCA scenarios of the type relating to 2 and 3 in this study, though caution should be taken in the interpretation of the results because of the limitation of comparing emissions that are not only uncertain and inconsistently modelled, but also happen in different times and places. Considering the significant uncertainty in characterising background data and other factors, it is justified to use simple batch test results (e.g. batch leaching test at L/S 10 as conservative estimate of the release) as the basis for LCA studies. However, since the L/S ratio appeared to be a very important parameter affecting the results, the use of percolation leaching test data is recommended, because it provides a relationship between L/S ratio and release. In smaller scenarios with a limited number of processes involved, the impact of modelling choices for leaching data was of importance. The choice of the L/S and pH condition appeared to affect mainly the LCA results. In such limited systems the impact of contaminant leaching determines the actual final results for toxic categories, but the number of important pollutants to be addressed further can be strongly reduced. Thus, in such scenarios, the LCA practitioner should have an understanding of the release mechanisms and of possible parameters (e.g. pH) which can have significant effects on the results, and data from percolation leaching tests combined with pH dependent data are recommended. Absolute values relating to impacts in LCAs do not stand by themselves, but they nevertheless have a comparative value. Thus, when comparing two residual materials to be used in a similar scenario, or when comparing impacts from leaching with impacts from other activities in the system, consistency in data quality, modelling and coverage is crucial, and the awareness of these aspects must guide the LCA practitioner in choosing a reasonable level of detail while dealing with a variety of scenarios and available data. Such an approach leads to a fair screening of a system, which could then be investigated further in its individual parts (e.g. leaching from BA in road) by applying other methodologies (e.g. risk assessment) and thereby complementing the LCA results.

4. Conclusions This study combined a detailed evaluation of LCA approaches for modelling leaching and discussions on the leaching of pollutants from mineral residues aiming at evaluating the importance of leaching emission data in LCA of WMSs. Using MSWI BA as a case material in three LCA scenarios, it was found that leaching data could not be disregarded in LCA studies of WMSs. The results were determined by large uncertainties in the LCI data of background activities (such as electricity production) in scenarios with large system boundaries. Although pollutant release was consistent with known leaching mechanisms, leaching data were characterised by significant variability, due to the intrinsic variability of the sample materials and to uncertainty surrounding the definition of the utilisation scenario. An innovative method to assess contaminant transport into the environment was applied, in order to improve the determination of transfer coefficients into the soil

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and water compartments for use in an LCA. The transport of contaminants into the natural environment, metal speciation and release conditions (e.g. pH, L/S) in the utilisation scenario were of importance, and the leaching impact could be associated with a limited number of contaminants (e.g. As, Cr, Cu, Mo, Sb, and Zn for MSWI BA) which should be investigated further within an LCA study. Thus, leaching from residual material is important; however, the level of detail in its modelling within an LCA should be consistent with the accuracy of modelling of other relevant emissions in the scenario, in order to avoid unfair and misleading assessments. Acknowledgements This research was partially funded by the Danish Research Council as a part of the IRMAR (Integrated Resource Management & Recovery) initiative. AFATEK Ltd. is acknowledged for providing access to the leaching database. Roberto Turconi and Anders Damgaard (DTU Environment) are acknowledged for constructive discussions and Hiroko Yoshida for helping with the statistics. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.wasman.2014. 12.018. References Apul, D.S., Gardner, K.H., Eighmy, T.T., 2007. Modeling hydrology and reactive transport in roads: the effect of cracks, the edge, and contaminant properties. Waste Manage. 27, 1465–1475. Astrup, T., 2007. Pretreatment and utilization of waste incineration bottom ashes: danish experiences. Waste Manage. 27, 1452–1457. Astrup, F.T., Pedersen, A.J., Hyks, J., Frandsen, J.F., 2010. Residues from waste incineration. PSO-5784. Bakas, I., Astrup, T.F., Hauschild, M.Z., Rosenbaum, R.K., Heavy metal emissions from landfills and their influence on LCA-based environmental assessments. In: Proceedings Sardinia 2013, 14th International Waste Management and Landfill Symposium. 30 September – 4 October, 2013, S. Margherita di Pula, Cagliari, Italy. CISA Publisher, Italy. Bendz, D., Arm, M., Flyhammer, P., Westberg, G., Sjöstrand, K., Lyth, M., Wik, O., 2006. The Vändöra Test Road, Sweden: A Case Study of Long-term Properties of a Road Constructed with MSWI Bottom Ash, Q4-241 (in Swedish). Bendz, D., Suer, P., van der Sloot, H.A., Kosson, D.S., Flyhammar, P., 2009. Modelling of Leaching and Geochemical Processes in an Aged MSWIBA Subbase Layer, Report Q6-648, VÄRMEFORSK Service AB, Stockholm, Sweden. Birgisdóttir, H., 2005. Life cycle assessment model for road construction and use of residues from waste incineration. PhD Thesis, Department of Environmental Engineering, Technical University of Denmark. Birgisdóttir, H., Bhander, G., Hauschild, M.Z., Christensen, T.H., 2007. Life cycle assessment of disposal of residues from municipal solid waste incineration: recycling of bottom ash in road construction or landfilling in Denmark evaluated in the ROAD-RES model. Waste Manage. 27, S75–S84. Boyd, T.J., Wolgast, D.M., Rivera-Duarte, I., Holm-Hansen, O., Hewes, C.D., Zirino, A., Chadwick, D.B., 2005. Effects of dissolved and complexed copper on heterotrophic bacterial production in San Diego Bay. Microb. Ecol. 49, 353–366. Building Materials Decree, 1995. Staatsblad van het Koninkrijk der Nederlanden, vol. 567, pp. 1–92. Carpenter, A.C., Gardner, K.H., Fopiano, J., Benson, C.H., Edil, T.B., 2007. Life cycle based risk assessment of recycled materials in roadway construction. Waste Manage. 27, 1458–1464. Carpenter, A., Jambeck, J.R., Gardner, K., Weitz, K., 2013. Life cycle assessment of end-of-life management options for construction and demolition debris. J. Ind. Ecol. 17, 396–406. Christensen, T.H., Bhander, G., Lindvall, H., Larsen, A.W., Fruergaard, T., Damgaard, A., Manfredi, S., Boldrin, A., Riber, C., Hauschild, M., 2007. Experience with the use of LCA-modelling (EASEWASTE) in waste management. Waste Manage. Res. 25, 257–262. Cooper, C.A., Tait, T., Gray, H., Cimprich, G., Santore, R.C., McGeer, J.C., Wood, C.M., Smith, D.S., 2014. Influence of salinity and dissolved organic carbon on acute Cu toxicity to the rotifer Brachionus plicatilis. Environ. Sci. Technol. 48, 1213–1221. Cornelis, G., Johnson, C.A., Gerven, T.V., Vandecasteele, C., 2008. Leaching mechanisms of oxyanionic metalloid and metal species in alkaline solid wastes: a review. Appl. Geochem. 23, 955–976. CPR, 305/2011/EU. Construction Products Regulation: Regulation (EU) No 305/2011 of the European Parliament and of the Council of 9 March 2011 Laying Down

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Harmonised Conditions and Repealing Council Directive 89/106/EEC. . Dabo, D., Badreddine, R., De Windt, L., Drouadaine, I., 2009. Ten-year chemical evolution of leachate and municipal solid waste incineration bottom ash used in a test road site. J. Hazard. Mater. 172, 904–913. Danish Road Directorate. 2012. Utilisation of bottom ash from waste incineration – guideline (Bundsikringslag af forbrændingsslagge – Vejledning). In Danish. Retrieved from vejregler.lovportaler.dk. Dawson, A., 2009. Water in road structures: movement, drainage and effects. Geotechnical, Geological and Earthquake Engineering 5. Dijkstra, J.J., Meeussen, J.C.L., Comans, R.N.J., 2004. Leaching of heavy metals from contaminated soils: an experimental and modeling study. Environ. Sci. Technol. 38, 4390–4395. Dijkstra, J.J., Van der Sloot, H.A., Comans, R.N.J., 2006. The leaching of major and trace elements from MSWI bottom ash as a function of pH and time. Appl. Geochem. 21, 335–351. 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