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Using function evaluation in multi-criteria decision analysis for sustainability appraisal of remediation alternatives

This document has been downloaded from Chalmers Publication Library (CPL). It is the author´s version of a work that was accepted for publication in: Science of the Total Environment (ISSN: 0048-9697)

Citation for the published paper: Volchko, Y. ; Norrman, J. ; Rosén, L. et al. (2014) "Using soil function evaluation in multi- criteria decision analysis for sustainability appraisal of remediation alternatives". Science of the Total Environment, vol. 485-486 pp. 785-791.

http://dx.doi.org/10.1016/j.scitotenv.2014.01.087

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USING SOIL FUNCTION EVALUATION IN MULTI CRITERIA DECISION ANALYSIS 1 FOR SUSTAINABILITY APPRAISAL OF REMEDIATION ALTERNATIVES 2 Yevheniya Volchkoa, Jenny Norrmana, Lars Roséna, Magnus Bergknutbc, Tore Söderqvistd, Tommy 3 Norberge, Sarah Josefssonf, Karin Wibergf, Mats Tysklindb 4 5 a Department of Civil and Environmental Engineering, Chalmers University of Technology, Sven Hultins 6 7 gata 8, SE-41296, Gothenburg, Sweden 8 [email protected] 9 10 bDepartment of Chemistry, Umeå University, SE-901 87 Umeå, Sweden 11 12 c Envix, SE-906 20, Umeå, Sweden 13 14 d 15 Enveco Environmental Economics Consultancy Ltd., Måsholmstorget 3, SE-127 48 Skärholmen, 16 Sweden 17 18 eMathematical Sciences, Chalmers University of Technology & University of Gothenburg, SE-412 96 19 Gothenburg, Sweden 20 21 f 22 Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Box 23 7050 SE-750 07, Uppsala, Sweden 24 25 Abstract 26 27 Soil functions are critical for survival and thus for ‟ provision of services to 28 29 humans. In 2006, the proposed EU Soil Framework Directive introduced ecological, social and 30 economic soil functions to be accounted for in practice. Ecological soil functions are 31 a product of the complex interactions between living (biological) and non-living (physical and chemical) 32 soil components. Social and economic soil functions are the benefits humans gain from ecosystems. 33 Once a soil function is utilized by humans, it is usually called an . One of the major 34 threats constraining proper functioning of the soil and thus provision of ecosystem services is soil 35 36 contamination. Remedial actions typically only address the chemical . However, emerging 37 regulatory requirements demand a holistic view on soil evaluation in remediation projects. Based on a 38 prototype for sustainability appraisal of remediation alternatives using Multi-Criteria Decision Analysis 39 (MCDA), this paper presents a structured and transparent approach for incorporating the soil function 40 concept into a set of ecological, socio-cultural and economic criteria of the MCDA. The approach 41 suggests using (1) soil quality indicators (SQI – physical, chemical and biological soil properties) for 42 43 evaluating effects of remediation on soil functions in the ecological domain, and (2) value-related 44 measurements (e.g. attitudes, opinions, and willingness to pay) for exploring the effects on ecosystem 45 services resulting from soil functions in the socio-cultural and economic domains of sustainability. The 46 specific objective of this study is to test applicability of a preliminary minimum data set (MDS) of soil 47 quality indicators for soil function evaluation using a case study site located in Marieberg, northern 48 Sweden. The suggested MDS consists of , content of coarse material, available water, 49 50 organic matter content, potentially mineralizable nitrogen, pH, available phosphorus, and contaminant 51 concentration. Although the soil at the Marieberg site is heavily contaminated with dioxins, it provides 52 potentially favourable conditions for soil functioning with some limitations. Potentially mineralizable 53 nitrogen indicates limited biological activity in the soil. On the other hand, content of coarse material, 54 pH and available water capacity provide potentially favorable conditions for biological activity. 55 Available phosphorus and content of organic matter limit . 56 57 58 Keywords: remediation, soil functions, soil ecosystem services, sustainability assessment, multi- 59 criteria decision analysis, contaminated sites/soil, soil quality indicators 60 1 61 62 63 64 65 1. Introduction 1 2 1.1. Background 3 4 Soil functions are critical for ecosystem survival and thus for ecosystems‟ provision of services to 5 6 humans. The components of an ecosystem are dependent on a healthy soil to enable the entire 7 8 ecosystem to function properly. The close relationship between soil functions and the ecosystem were 9 10 recognized by scientists in the 1970-s (e.g. Lehmann and Stahr, 2010) and by politicians and decision- 11 12 makers in more recent years (COM, 2006). In 2006, the proposed EU Soil Framework Directive 13 14 introduced seven ecological, social and economic soil functions to be accounted for in land 15 16 management practice. These include (i) biomass production, including and forestry; (ii) 17 18 storing, filtering and transforming nutrients, substances and water; (iii) biodiversity pool, such as 19 20 , species and genes; (iv) physical and cultural environment for humans and human activities; 21 22 (v) source of raw materials; (vi) acting as carbon pool; (vii) archive of geological and archeological 23 24 heritage (COM, 2006). Being a subset of ecosystem functions, a soil function can be defined asa soil 25 26 ecosystem service once it is utilized for benefit by humans (Dominati et al., 2010). 27 28 29 30 To describe ecological soil functions, soil scientists use soil quality indicators (SQIs) to address the 31 32 physical, the chemical, and the biological soil elements with an equal degree of importance (e.g. 33 34 Andrews et al., 2004; Doran and Zeiss, 2000; Schindelbeck et al., 2008). Balanced interconnections of 35 36 these three soil quality elements are at the core of properly functioning soil. Unfortunately, inherited 37 38 from the era of industrialization, is a widespread threat to proper soil functioning 39 40 throughout the world. Striving for addressing soil contamination, research and technical development 41 42 in recent decades has resulted in a wide palette of available remediation techniques to improve 43 44 chemical soil quality, i.e. reduction of contaminant concentrations and amounts in the soil to tolerable 45 46 levels guided by intended land uses (e.g. Swedish EPA, 2009). However, when evaluating the 47 48 sustainability of remediation alternatives, it becomes important to consider the unity of all three soil 49 50 quality elements (composed of chemical, as well as physical and biological properties) for ensuring 51 52 that the ecological effects on soil functions are properly taken into account. A Multi Criteria Decision 53 54 Analysis (MCDA) approach has been suggested by several authors (e.g. CL:AIRE, 2011; Linkov et al., 55 56 2006; Rosén et al., 2009, 2013; Sparrevik et al., 2011) to support decision-making on sustainable 57 58 remediation accounting for both positive and negative effects of available remediation alternatives. 59 60 2 61 62 63 64 65 MCDA methods for sustainability appraisal of remediation alternatives can provide a structure for 1 2 evaluating sustainability considering a set of ecological, socio-cultural and economic criteria. 3 4 5 6 1.2. Aim and paper outline 7 8 This paper presents an approach to soil function evaluation for facilitating input on ecological effects of 9 10 remediation alternatives in an MCDA framework for sustainability appraisal described by Norrman et 11 12 al. (2012); Rosén et al. (2009; 2013). First, a brief description of the MCDA framework that forms the 13 14 point of departure in this study is given in Section 2.1. Further, the paper describes an approach to soil 15 16 function evaluation within the MCDA framework and presents a preliminary minimum data set (MDS) 17 18 for soil function evaluation (Section 2.2). Using the suggested MDS, the effects of remediation 19 20 alternatives on soil functions are evaluated for the site located in Marieberg, northern Sweden (Section 21 22 3). The final part of the paper discusses uncertainties associated with soil function evaluation and 23 24 provides some concluding remarks (Section 4). 25 26 27 28 2. A Multi Criteria Decision Analysis for sustainability appraisal 29 30 2.1 The MCDA framework 31 32 The MCDA framework developed by Rosén et al. (2009; 2013) uses the common sustainability model 33 34 of the three pillars: ecology, economy, and socio-culture. In the economic domain, costs and benefits 35 36 are measured quantitatively in monetary terms using Cost-Benefit Analysis (CBA) addressing the 37 38 social profitability criterion (Rosèn et al., 2008). In the ecological and socio-cultural domains however, 39 40 qualitative scores are assigned to a number of key criteria (Table 1). Each criterion is scored between 41 42 -2 representing “very negative effect” and +2 representing “very positive effect” relative to a reference 43 44 alternative. A score of 0 represents “no effect”. Importantly, the effects of remediation alternatives are 45 46 measured relative to the reference alternative, e.g. when no remedial action is taken. 47 48 49 The MCDA framework is based on a linear additive model (to rank the remediation alternatives) in 50 51 52 combination with a non-compensatory method (to identify those alternatives which are regarded as not 53 54 leading towards sustainability). The score of each criterion are added and integrated, together with the 55 56 results of the CBA, into a normalized sustainability index. The most sustainable alternative is the one 57 58 which generates the highest sustainability index. 59 60 3 61 62 63 64 65

1 2 Uncertainty analysis is an important part of the MCDA model (Norrman et al., 2012; Rosén et al. 3 4 2013). The uncertainties in the model are treated with help of a Monte Carlo simulation. Monte Carlo 5 6 simulation is a technique for calculating uncertainties in the model results by repeatedly picking values 7 8 from the probability distribution for the uncertain variables in the model (CB, 2010). The assignment of 9 10 the uncertainty distribution in the MCDA model is suggested to be performed in three steps: (1) 11 12 selection of distribution type, i.e. selection of whether all types of effects, only positive, or only negative 13 14 effects are possible for the specific criterion; (2) estimation of the most likely effect using the scale 15 16 presented above; and (3) assigning the uncertainty level of the estimation of the most likely effect 17 18 (Rosén et al., 2013). The three-step procedure results in a probability distribution representing the 19 20 uncertainty of the scoring of criteria in the ecological and the social domains of the MCDA. Lognormal 21 22 distributions are assigned to the cost and benefit items in the economic domain of the MCDA. Further, 23 24 a Monte Carlo simulation is performed to calculate a probability distribution for the normalized 25 26 sustainability index. 27 28 29 30 2.2 Soil function evaluation within the MCDA framework 31 32 To comply with the proposed EU Soil Framework Directive, the MCDA framework by Rosèn et al. 33 34 (2009; 2013) aims to include soil function evaluation in the decision process. For integrating the soil 35 36 function evaluation into sustainability appraisal of remediation alternatives, it is important to make a 37 38 distinction between the ecological soil functions and the soil ecosystem services. The latter are related 39 40 to the socio-economic effects. The effects of remediation on soil ecosystem services (iv) physical and 41 42 cultural environment for humans and human activities (including recreation, aspiration services), and 43 44 (vii) archive of geological and archeological heritage are addressed in the socio-cultural domain of the 45 46 MCDA model. These soil ecosystem services correspond to and are to some extent covered by Local 47 48 environmental quality and amenity and Cultural heritage criteria respectively (Table 1). The soil 49 50 abilities to serve as a (v) source of raw materials and to act as a (vi) carbon pool can be monetized 51 52 and addressed in the economic domain of the MCDA model. (i) Biomass production including 53 54 agriculture and forestry is not considered relevant in soil remediation projects. The effects on 55 56 ecosystem services resulting from changes in soil quality are suggested to be evaluated using value- 57 58 related measurements. These measurements can be expressed in: (1) community-based values which 59 60 4 61 62 63 64 65 reflect attitudes, preferences, and intentions associated with a soil ecosystem service; (2) economic 1 2 values revealed by market data (if any) about a soil ecosystem service, or the willingness to pay 3 4 (WTP) for the service provided by the end use of the soil (SAB, 2009). 5 6 7 8 The ecological soil functions are related to the internal functioning of a soil system. The effects of 9 10 remediation alternatives on such ecological soil functions as (ii) storing, filtering and transforming 11 12 nutrients, substances and water, and (iii) biodiversity pool such as habitats, species and genes are to 13 14 some extent addressed by the Soil criterion of the MCDA model (Table 1). Together with conventional 15 16 risk assessment methods, soil quality indicators (SQIs), i.e. physical, chemical and biological soil 17 18 properties, are used for evaluation of the effects of remediation alternatives on soil functions. A 19 20 suggested minimum data set (MDS) for ecological soil function evaluation is presented in Table 2. The 21 22 MDS was identified based on a literature review, primarily considering the works by Bone et al. (2010); 23 24 Brown et al. (2005), Craul and Craul (2006), Doni et al. (2012), Epelde et al. (2008a,b; 2009; 2010a,b), 25 26 Gugino et al. (2009), Jelnsic et al. (2013), Lehmann et al. (2008), Pazos et al. (2012); Schindelbeck et 27 28 al. (2008), and van Herwijen et al. (2007). The suggested MDS is identified by compiling SQIs that are 29 30 (1) suggested by three or more literature sources in the studies exploring the effects on ecological soil 31 32 functions in remediation projects, and (2) consistent with the MDSs for the purposes other than 33 34 agricultural productivity of land (Bone et al., 2010; Craul and Craul, 2006; Lehmann et al., 2008; 35 36 Schindelbeck et al., 2008). 37 38 39 40 3. Marieberg case study: Evaluation of the effects on ecological soil functions 41 42 The former Marieberg saw mill site is situated in northern Sweden (Aberg et al. 2010) and covers an 43 44 area of approximately 1500 m x 150 m. Chlorophenol (CP) based wood preservatives was used for 45 46 more than two decades until closure of the activities in 1970. The CP formulations were contaminated 47 48 with polychlorinated-p-dibenzodioxins and dibenzofurans (PCDD/Fs). The CP formulations were 49 50 contaminated with polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs, commonly called 51 52 „dioxins‟). These compounds are highly persistent organic pollutants, which tend to partition to 53 54 particles rather than being dissolved and mobilized by water (Persson et al., 2008). As a result, the 55 56 site is still heavily polluted with PCDD/Fs from many different activities (Aberg et al. 2010). The site 57 58 includes areas that were used for sawing, impregnation, indoor storage, indoor drying, and an outdoor 59 60 5 61 62 63 64 65 timber yard. Inside and just outside the area, there are residential houses, pastures for dairy cows, a 1 2 farm, a hostel, and a camping area. In a previous risk assessment study of the site (Aberg et al. 2010), 3 4 human exposure to PCDD/Fs through a broad spectrum of exposure pathways was assessed. Soil, 5 6 air, , raspberries, carrots, potatoes, grass, milk, and eggs were analyzed for the content of 7 8 PCDD/Fs, and the results showed that most exposure media were clearly elevated as compared to 9 10 national reference samples. The calculated exposure levels showed that a number of site-specific 11 12 exposure routes can be of importance for people residing this area. Thus, despite low mobility of 13 14 PCDD/Fs, these contaminants can be transferred from the polluted soil to other environmental media 15 16 and into humans. 17 18 19 20 3.1 Remediation alternatives 21 22 Three remediation alternatives are considered within this study (Table 3). 23 24 25 26 3.2 Soil sampling and analysis 27 28 Soil was randomly sampled to a depth of 0.5 m, covering the entire saw mill area. In total, 18 samples 29 30 were collected. The samples were handled according to ISO 10381-6 and sent to certified laboratories 31 32 for analysis of the concentrations of the 2,3,7,8- substituted CDD/Fs (using the isotope dilution 33 34 method, with isotopically labelled standards for all 17 2,3,7,8-CDD/F congeners), and of the suggested 35 36 SQIs, i.e. particle size distribution (ISO 3310-2),organic matter content (SS-EN 12879), potentially 37 38 mineralizable nitrogen (Standard Methods 18th ed., 4500 NH3-B), pH (ISO 10390), and available 39 40 phosphorus (AL-P, Egner et al., 1960 and SS 02 8310). Available water capacity was determined as a 41 42 function of a FAO (Food and Agriculture Organization of the United Nations) soil texture class, organic 43 44 matter content and bulk density (Lehmann et al., 2008). 45 46 47 48 3.3 Evaluation of the effects on ecological soil functions 49 50 The evaluation of the effects on ecological soil functions is performed in a separate module. The 51 52 output of this module provides input to the MCDA framework addressing the effects of remediation 53 54 alternatives on soil functions. The procedure for evaluation of the effects on soil functions includes the 55 56 three steps described below. 57 58 59 60 6 61 62 63 64 65 Step 1. (into one of five classes) for a reference remediation alternative according to 1 2 the methodology suggested by Gugino et al. (2009), Idowu et al. (2008), Schindelbeck et al. (2008), 3 4 Volchko (2013). This methodology implies scoring of each SQI in the MDS in the interval [0; 1] where 5 6 the scores of [1; 0.71], [0.7; 0.31], [0.3; 0] represent of good, medium and poor qualities, 7 8 respectively. Thereafter the scores are integrated into a total index using the arithmetic mean values of 9 10 the scores. The total index forms a basis for soil classification into five soil classes: very good, good, 11 12 medium, poor, and very poor (Gugino et al., 2009; Volchko, 2013). Soil texture and contaminant 13 14 concentration are not scored. However, soil texture is used to determine the available water capacity 15 16 as well as the scores for potentially mineralizable nitrogen and available phosphorus. Contaminant 17 18 concentration is used to evaluate the ecotoxicological risk and not included in the soil function index. 19 20 Step 2. Evaluation of the effects of available remediation alternatives on soil functions relative to the 21 22 reference alternative using a suggested matrix of the effects on soil functions (Fig. 1). The effects are 23 24 scored between -2 representing “very negative effect” and +2 representing “very positive effect” 25 26 relative to a reference alternative. A score of 0 represents “no effect”. 27 28 Step 3. Uncertainty and sensitivity analyses of the resulting scores. 29 30 31 32 3.4 Results 33 34 The studied soils at the case study site were and sandy according to a FAO taxonomy 35 36 triangle. The evaluated SQIs for Reference Alternative are compiled in Table 4. Using the 37 38 methodology for soil classification by Gugino et al. (2009), a mean index of 0.58 corresponding to soil 39 40 class 3 and a medium soil function performance was computed for the analyzed soil samples. The 41 42 mean score for potentially mineralizable nitrogen of 0.02 indicates limited biological activity in the soil. 43 44 On the other hand, the mean scores for content of coarse material, pH and available water capacity 45 46 were 0.85, 0.74 and 0.9 respectively, providing potentially favorable conditions for biological activity. 47 48 The scores for available phosphorus and content of organic matter were 0.52 and 0.41 respectively 49 50 thus limiting e.g. soil fertility. 51 52

53 54 A preliminary correlation analysis indicates dependencies between concentration of dioxins and 55 56 organic matter content. A strong correlation between surface and organic pollutants 57 58 has also been reported in other studies (e.g. Bergknut et al., 2010; Meijer et al., 2002; Meijer et al., 59 60 7 61 62 63 64 65 2003), but for sites contaminated directly from industrial activities rather than from atmospheric 1 2 deposition, this correlation is expected to be less pronounced. 3 4 5 6 The effects of remediation alternatives on soil functions (i.e. assigned scores) were evaluated relative 7 8 to the reference alternative using a suggested matrix of the effects (Fig. 1). The remediation 9 10 alternative Conservation of the site as “Environmental Risk Area”, which does not include any remedial 11 12 action, will generate no effect, i.e. a score of 0. Depending on the soil class of the refilling material, the 13 14 alternatives Excavation and Excavation of Hot Spots will generate different effects on soil functions 15 16 and accordingly different scores (Table 5). Obviously, the quality of the selected refilling material 17 18 becomes a crucial factor for the future soil functioning. 19 20 21 22 3.5. Uncertainty analysis for soil classification at the Marieberg site 23 24 The soil classification was based on scoring of the SQIs in the suggested MDS and integrated into a 25 26 soil function index that corresponds to one of five soil classes. Lognormal distributions were used to 27 28 represent the uncertainties in the SQIs. First, the measured value (assigned as the mode) of the SQI 29 30 was transformed into the mean on the lognormal scale. Further, the 95th percentile was computed 31 32 representing the upper uncertainty level in the measurement of the SQI reported by the laboratory. For 33 34 example, the uncertainties in the measurements of pH, organic matter content, and potentially 35 36 mineralizable nitrogen were ±0.2 units, ±15%, ±20% respectively. The lognormal distributions were 37 38 assigned using a location of 0, the computed mean on the lognormal scale and the computed 95th 39 40 percentile. Thereafter, a Monte Carlo Simulation (performed with the Oracle Crystal Ball© add-in 41 42 software) was run to analyze the uncertainties in the resulting soil class for Reference Alternative. The 43 44 simulation results showed that with almost 100% certainty, the simulated total soil function index 45 46 corresponds to soil class 3, representing medium soil function performance (Fig. 2). 47 48 Using Oracle Crystal Ball©, a sensitivity analysis was done to investigate the contribution from each 49 50 input variable, i.e. SQIs, to the total uncertainty (Fig. 3). Available phosphorus and pH in soils were the 51 52 most sensitive input variables in the model that contribute most to the total uncertainty. 53 54

55 56 57 58 59 60 8 61 62 63 64 65 4. Discussion and concluding remarks 1 2 This paper shows the important distinction between soil functions and the soil ecosystem services 3 4 (Section 2.2). The ecological soil functions are related to the internal functioning of a soil system, i.e. 5 6 what the soil does in its natural state. The soil ecosystem services are related to the benefits humans 7 8 gain from ecosystems. Further, in accordance with emerging regulatory requirements on soil 9 10 protection, this work shows how effects on soil functions can be included in sustainability appraisal of 11 12 remediation alternatives. This paper only addresses ecological effects, but in a full sustainability 13 14 appraisal, the effects on soil ecosystem services also have to be taken into consideration. Such effects 15 16 have to be addressed in the socio-cultural and economic domains, e.g. by evaluating the impact of 17 18 these effects on Local environmental quality and amenity, Cultural heritage and Social profitability, 19 20 using CBA (Table 1). To meet emerging regulatory requirements on soil protection, the MCDA for 21 22 sustainability appraisal of remediation alternatives suggests using (1) physical, chemical, and 23 24 biological soil quality indicators (SQIs) for addressing the effects on ecological soil functions in the 25 26 ecological domain, and (2) value-related measurements for addressing the effects on soil ecosystem 27 28 services in the socio-cultural and the economic domains of the MCDA. The suggested MDS of SQIs 29 30 consists of soil texture, content of coarse material, available water, organic matter content, potentially 31 32 mineralizable nitrogen, pH, available phosphorus, and contaminant concentration (Table 2). This study 33 34 shows how these SQIs can be practically used in sustainability appraisal of remediation alternatives. 35 36 Contaminant concentration is not included into the soil function index. This particular SQI is a key 37 38 factor of ecotoxicological risk. Therefore, the results of ecological soil function evaluation, described in 39 40 this paper, and ecotoxicological risk assessment are combined in the MCDA model addressing the 41 42 Soil criterion. 43 44 45 46 In Sweden, excavation is the most common remediation technology (van Hees, 2008). The material 47 48 used for refilling can vary from the crushed to glacial till. Since the effects of remediation 49 50 alternatives on soil functions, i.e. scores, strongly depend on the soil quality class of the refilling 51 52 material, the resulting scores presented in Table 5 are associated with large uncertainties. Thus, it is 53 54 reasonable to assign probability distributions for the scores instead of point values, in accordance with 55 56 the assessment of criteria in the MCDA (Norrman et al., 2012; Rosén et al., 2013). The assignment of 57 58 the uncertainty distribution in the MCDA model is suggested to be performed in three steps: (1) 59 60 9 61 62 63 64 65 selection of distribution type, i.e. selection of whether all types of effects, only positive, or only negative 1 2 effects are possible for the specific criterion; (2) estimation of the most likely effect; and (3) assigning 3 4 the uncertainty level of the estimation of the most likely effect. For example, if the soil of class 3 5 6 (medium soil function performance) will be substituted with a refilling material of poor quality then no 7 8 positive scores are possible. Instead, there is a negative effect on the soil functions with a low level of 9 10 uncertainty. On the other hand, if the same soil will be substituted with a refilling material of good 11 12 quality, no negative scores are possible, and there is a positive effect on the soil functions with a low 13 14 level of uncertainty. Further, if nothing is known about the refilling material, then all scores are 15 16 possible, and e.g. the most likely effect on soil functions is positive with a medium level of uncertainty, 17 18 or the most likely effect on soil functions is very positive with a high level of uncertainty. Thus, the 19 20 effects of soil excavation on ecological soil functions strongly depend on the type of refilling material. 21 22 Together with conventional risk assessment methods, the effects on ecological soil functions (i.e. the 23 24 effects on physical, chemical and biological soil properties) of the refilling material should be 25 26 thoroughly considered in remediation projects in order to meet emerging regulatory requirements on 27 28 soil protection. 29 30 31 32 Remediation is usually aimed at reducing risks of negative impact on human health and the 33 34 environment posed by contaminants in the soil. The level of risk reduction is typically linked to the 35 36 intended , i.e. reduction of contaminant concentrations and amounts in the soil to tolerable 37 38 levels guided by the end use of the site. For example, chemical soil quality requirements for the 39 40 sensitive land uses such as residential and green areas are stricter than for the less sensitive land 41 42 uses such as industrial areas, parking spaces and roads. The end use of the site will, however, also 43 44 have a direct impact on the targeted soil functions and in turn on the resulting soil ecosystem services. 45 46 For this reason, identification of the end use of the site (i.e. land use scenarios) should be done early 47 48 in the sustainability assessment. Once land uses and a corresponding set of soil functions and 49 50 services are identified, the suggested SQIs and relevant value-related measurements can be used to 51 52 evaluate effects associated with available remediation alternatives. The suggested MDS of SQIs is 53 54 therefore particularly relevant to the future green areas within a remediation site. 55 56

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Landscape and Urban 8 9 Planning 2008; 88: 73-80. 10 Sparrevik M, Barton DN, Bates M E, Linkov I. Use of stochastic multi-criteria decision analysis to 11 12 support sustainable management of contaminated sediments. Environmental Science & 13 Technology 2011; 46: 1326-34. 14 15 SS 02 8310 P-AL method. (In Swedish: P-AL metoden). Swedish Standards Institute; 1993. 16 SS-EN 12879 Slam characterization – Loss on ignition. (In Swedish: Karaktärisering av slam - 17 18 Bestämning av torrsubstansförlust vid upphettning.) Swedish Standards Institute. 19 Standard Methods: For the Examination of Water and Wastewater, 18th ed. 1992. 20 21 Swedish EPA. Guideline values for contaminated land: Model description and a guide. (In Swedish: 22 Riktvärden för förorenad mark: Modellbeskrivning och vägledning.) Report 5976. Stockholm: 23 24 Naturvordsvärket; 2009. 25 van Hees PAW, Elgh-Dalgren K, Engwall M, von Kronhelm T. Re-cycling of remediated soil in 26 27 Sweden: An environmental advantage? Resources, Conservation and Recycling 2008; 52: 28 29 1349-61. 30 van Herwijnen R, Al-Tabbaa A, Hutchings TR, Moffat AJ, Ouki SK, Johns ML. The Impact of Waste 31 32 Compost-Based Soil Amendments on the Leaching Behavior of a Heavy Metal Contaminated 33 Soil. Environmental engineering science 2007; 24 (7): 897-904. 34 35 Volchko Y. SF Box – A tool for evaluating ecological soil functions in remediation projects. Report 36 2013:1, ISSN 1652-9162. Gothenburg: Chalmers Reproservice; 2013. 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 14 61 62 63 64 65 Table Click here to download Table: Table 1.docx

Table 1 Key criteria of the updated MCDA framework by Rosén et al. (2013). Environmental domain Socio-cultural domain Economic domain  Soil  Local environmental  Social profitability  Flora and fauna quality and amenity  Groundwater  Cultural heritage  Surface water  Equity  Sediment  Health and safety  Air  Local participation  Non-renewable natural resources  Local acceptance  Non-recyclable waste

Table Click here to download Table: Table 2.docx

Table 2 A suggested minimum data set for evaluation of the ecological soil functions in remediation projects. Soil Quality Indicators (SQIs) Soil texture Content of coarse material Available water capacity Organic matter content Potentially mineralizable nitrogen pH Available phosphorus

Table Click here to download Table: Table 3.docx

Table 3 Remediation alternatives at the Marieberg site. Remediation Comment alternative Reference Alternative No remedial action taken. Excavation The alternative assumes replacement of the contaminated soil with a clean soil. This alternative has been decided on by the authorities and will be executed in 2013/2014. Excavation of Hot Spots The alternative assumes replacement of the heavily contaminated soil in the hot spots with a clean soil. This alternative has been discussed by the authorities but was in the specific case not chosen mainly due to concerns connected with future land use. Conservation of the site The alternative prohibits access to the area by fences and assumes no as “Environmental Risk remedial action. This alternative is a part of the presented research Area” project. The alternative is possible within current legislation and is therefore of interest, especially concerning the effects on soil functions and the socio-cultural domain.

Table Click here to download Table: Table 4.docx

Table 4 Basic statistics for the analyzed soil quality indicators at the Marieberg site. Sum PCDD/F 17, NH4-N, AL-P, AW,[%] CM,[%] OM,[%] pH [pg/g] [mg/kg] [mg/kg] No 18 18 18 18 18 18 16* Mean 31 000 22 13 2.8 6.6 190 34 Std 46 000 1.5 6.5 2.2 0.7 25 10 Min 210 21 2 0.6 5.6 140 20 Max 160 000 25 21 7 8.2 230 59 CV 1.6 0.1 0.5 0.8 0.1 0.1 0.3 No: number of soil samples. Std: standard deviation. Max: maximum. Min: minimum. CV: coefficient of variation. CM: content of coarse material (ø>2 mm). AW: available water capacity. OM: organic matter content.

NH4-N: potentially mineralizable nitrogen. AL-P: available phosphorus. PCDD/F: dioxins. *The AL-P (available phosphorus) results for two soil samples textured sands are missing due to a mistake by the lab. Table Click here to download Table: Table 5.docx

Table 5 Possible scoring of the effects on soil performance after remediation at the Marieberg site as a function of the refilling material. Soil class of refilling material 1 2 3 4 5 Remediation alternatives ”Very good” ”Good” ”Medium” “Poor” ”Very poor” Excavation +2 +1 0 -1 -2 Excavation of Hot Spots +2 +1 0 -1 -2 +2: very positive effect. +1: positive effect. 0: no effect. -1: negative effect. -2: very negative effect. Figure Click here to download Figure: Figure 1.docx

Soil Class after Remediation Soil Quality Classes: Reference 1 Very good Soil Class 1* 2* 3* 4* 5* 2 Good 3 Medium 1 0 -1 -2 -2 -2 4 Poor 5 Very poor 2 +1 0 -1 -2 -2

Effects on Soil Functions: 3 +2 +1 0 -1 -2 +2 Very positive +1 Positive 4 +2 +2 +1 0 -1 0 No effect -1 Negative 5 +2 +2 +2 +1 0 -2 Very negative Fig. 1. A suggested matrix of the effects on soil functions.

Figure Click here to download Figure: Figure 2.docx

Fig. 2. Soil classes for Reference Alternative at the Marieberg site.

Figure Click here to download Figure: Figure 3.docx

Fig. 3. Sensitivity chart for Reference Alternative showing the contribution of the input SQIs to the

total uncertainty in the resulting soil class.