Tao Xie, Reti Hai?, Xiangchun Quan, Anjie Li, Rui Liu, Yaxin Chen

1Beijing Engineering Research Center of Environmental Material for Water Purification, Beijing University of Chemical Technology, Beijing, China

2Institute of Resources and Environment Science, Mapuni, Beijing, China

3Key Laboratory of Water and Sediment Sciences of Ministry of Education/State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, China

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Soil Remediation Environmental Decision Support System Based on AHPPROMETHEE II Approach

Tao Xie1,2, Reti Hai1,?, Xiangchun Quan3, Anjie Li3, Rui Liu2, Yaxin Chen2

1Beijing Engineering Research Center of Environmental Material for Water Purification, Beijing University of Chemical Technology, Beijing, China

2Institute of Resources and Environment Science, Mapuni, Beijing, China

3Key Laboratory of Water and Sediment Sciences of Ministry of Education/State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, China

Submission Info

Communicated by Zhifeng Yang

Contaminated site remediation

Soil remediation technology selection

Environmental decision support system

AHP-PROMETHEE II approach

Selection of soil remediation technologies for contaminated sites is difficult given the large number of technologies available and the economic, social, and environmental impacts of site remediation activities. In this paper, we take into account a multitude of consequences and analyze their impacts with multi-criteria utility theory. By establishing the AHPPROMETHEE II （Analytic Hierarchy Process-Preference Ranking Organization Method for Enrichment Evaluations II） method, an Environmental Decision Support System （EDSS） has been developed for the purpose of identifying optimum soil remediation approaches for contaminated site. In the EDSS, AHP is used to structure the decision problem and to attribute weights to the criteria, whereas PROMETHEE is used to obtain a final ranking of the proposed alternatives. In addition, the hierarchical decision tree was constructed according to 4 criteria, i.e., time and economic costs, technical levels, technical applicability and social benefits, 12 indicators, and 21 soil remediation alternatives. The system has been applied to a case study, in which the best option of soil remediation technology for the particular remediation goal could be achieved. The effectiveness and shortcomings of this system are also discussed.

? 2015 L&H Scientific Publishing, LLC. All rights reserved.

1 Introduction

The problem of soil contamination in China has become more and more serious. Municipal and state agencies are facing with challenges in managing soil contamination resulting from the industrial age （Cai et al., 2008；Geng et al., 2001； Liang et al., 2012； Luo et al., 2011； Zhang et al., 2009）. In case of soil contamination, decision making on the application of remediation alternatives is a crucial step after a comprehensive analysis and assessment of contaminants and their impacts on safeguards have been conducted. The ability to make a correct or “good” decision develops with increasing experiences and knowledges about the consequences of thedecision and with the capability to comprehend and structure the decision problem （Scholz and Schnabel, 2006）.

Site soil remediation activities have their own economic, social, and environmental impacts, which raises a complex decision making process. Today, the multi-criteria decision analysis （MCDA） is increasingly used in EDSS as （1） it offers the possibility to deal with complex issues, （2） it incorporates criteria that are difficult to monetize, （3） it represents a holistic view incorporating tangible as well as intangible （or “fuzzier”） aspects, and （4） it enables the inclusion of stakeholders in the decision-making process （Turcksin et al., 2011）.

Historically, a range of generic decision support systems （DSSs） are available for decision-makers to decide whether and how the site should be subjected to control or remediation. Some widely used decision support software for contaminated site remediation, such as DESYRE （Decision support system for requalification of contaminated sites） （Sourdis et al., 2013, Stezar et al., 2013）, SMARTe （Sustainable management approaches and revitalization tools-electronic）, SADA （Stewart and Purucker, 2011） and so on., can be used to realize the function of environmental risk assessment, decision making support, etc., according to the management and assessment steps （Jiang et al., 2011； Sorvari and Sepp?l?, 2010）. However, due to the operation complexity, using the software often depends on a large number of site investigations, soil sample monitoring or pollutant transfer numeric simulation. In other words, an in-depth study is needed to fit the data requirement （Jiang et al., 2011； Rodrigues et al., 2009）. In addition, most operations are too complex and hard to apply in the sudden events for quick decision-making. Thus, to some extent, these features limit their applications in China.

The objective of our study is to develop a simple and flexible EDSS for the purpose of quickly identifying optimum soil remediation approaches for contaminated site. In this paper, a soil remediation technologies base is build according to the contaminated site remediation engineering experiences in practice both at home and abroad. An integrated approach is used that combines the Analytic Hierarchy Process （AHP） and the Preference Ranking Organization Method for Enrichment Evaluations （PROMETHEE）. AHP is used to structure the decision problem and to attribute weights to the criteria, whereas PROMETHEE is used to obtain a final ranking of the proposed alternatives.

Fig.1. Architecture of the soil remediation technology selection EDSS.

2 Methodology

2.1 General architecture of the EDSS

Figure 1 presented an overview of the architecture of the EDSS to select remediation technologies for con-taminated sites, showing its components and their interactions. The EDSS includes three basic components：user interface, AHP-PROMETHEE II module and data base. In the data base, evaluation criteria data base, soil remediation technologies base and parameters data base are connected one another.

The operational flowchart for the soil remediation technology selection was presented in Figure 2. By inputting the pollutant and contaminated site conditions, the system can give the scores of all technical alternatives for each indicator according to the evaluation criteria. And by selecting the preference conditions, the weights of each indicator can be set. Afterwards, the integrated score of each technical alternative can be calculated by means of the PROMETHEE II approach. Finally, the technical alternatives are ranked, and the recommendations towards the best compromise can be formulated.

Fig.2. Operational flowchart for the soil remediation technology selection EDSS based on AHP-PROMETHEE II approach.

2.2 Remediation technology selection decision

2.2.1 Hierarchical decision tree and weights of indicators

In this study, the hierarchical decision tree was constructed according to 4 criteria （B1： time and economic costs； B2： technical levels； B3： technical applicability for the site condition； B4： social benefits）, 12 indicators（C1～C12） （Figure 3）, and 21 soil remediation alternatives. The 12 indicators （C1～C12） were assigned weights, based on the AHP procedure （Li et al., 2012）.

Fig.3. Hierarchical decision tree of soil remediation technology selection decision.

In general, soil remediation techniques could be divided into two classes depending on whether the pollutant is directly removed or degraded in-place or not, i.e. in-situ or ex-situ. For a soil treatment technology, the cost indicator C1 was calculated by the excavation cost, treatment cost, equipment cost and transportation cost. Whereas for the in-situ soil remediation technology, excavation cost was almost zero. For the ex-situ soil remediation technologies, the soil excavation cost was calculated according to the project accounting method, which considered the impacts of the depth of pollutants and radius of pollution plume due to the risk assessment outcomes of the AOC. Since transportation costs involved many factors unrelated to the remediation technology itself, in this study it was ignored. In practical applications, it was needed to consider the transportation costs affected by the technology choices. The other eleven indicators （C2～C12） were obtained values with technical evaluation according to Table 1.

Table 1 Evaluation criteria of contaminated site remediation technical indicators.

2.2.2 AHP- PROMETHEE II approach

AHP is used to structure the decision problem and to attribute weights to the criteria（Saaty, 1987, Saaty, 1990）. Taking time control and cost control as priority respectively to account the indicator weight basing on the AHP method, results were shown in Table 2. In the process, for the criteria layer, CR=0.021. For the indicators layer, in the condition of cost control priority, CR=0.005, while in the condition of time control priority, CR=0.012.

Table 2 Weight of all indicators in hierarchy structure.

PROMETHEE is used to obtain a final ranking of the proposed alternatives. PROMETHEE was developed by Brans （Brans, et al., 1986）, which is the most well-known and widely applied outranking methods for pair wise comparison of the alternatives in each separate criterion. Three main PROMETHEE tools can be used to analyze the evaluation problem： 1） PROMETHEE I partial ranking, 2） PROMETHEE II complete ranking and 3） the GAIA （Geometrical Analysis for Interactive Aid） plane （Turcksin, et al., 2011）.In our study, the PROMETHEE II method was selected for the evaluation. This method started with the formulation of alternatives and a set of criterion. Then, it was formed as an m×n decision matrix. For each criterion, pair wise com-parison of alternatives aiand ajwas indicated by a preference indicator Pk（ai,aj）. This preference indicator was a function of the observed deviation between the scores of the alternatives on the considered criterion（Turcksin et al., 2011； Macharis et al., 2004）.

Pk（ai,aj） is pooled over the set of all criteria using Eq. （1）

wherekwis the weight of criterion k, and it is calculated by the AHP method.

Then the positive and negative outranking flows are calculated using Eq. （2） and Eq. （3）.

And the net dominance is calculated using equation.

The best alternative is the one with the highest net dominance.

A linear preference function Pk（ai,aj） is used to translate the difference between the evaluations （i.e., scores） obtained by two alternatives （aiand aj） in terms of a particular criterion, into a preference degree ranging from 0 to 1 in Eq. （5）（Li, et al., 2012）.

where dijis the deviation value between the alternatives aiand aj. For the cost indicator C1, q=0 and p=1000. For the other eleven indicators （C2～C12）, they are achieved by scoring with the same range, choose the preferences function parameters are as follows：q=0 and p=3.

3 Results & Discussion

Through the use of the procedures and technologies introduced above, we developed a EDSS for selecting remediation technologies for contaminated sites. In this part, the application of the EDSS was exemplified by using a suppositional case study.

3.1 Site overview

It is assumed that some industrial site existed sodium arsenate （Na2HAsO4.12H2O） pollution. The radius of the identified contamination plume to remedy is 4.5m； the depth of contamination is 3.5m. Its soil is yellowcinnamon soil （which belongs to clay loam）. The location was in a medium temperate and sub-humid region.Pollution treatment was in summer. The site was planned for industrial building in future. Some environment sensitive points existed in its surrounding area.

3.2 Results

By inputting pollutant, contaminated site type, soil type, site use, climate zone, humidity and contaminated range, which can be calculated according to the pollution distribution survey and risk assessment, remediation technology decision can be made. The contaminated site information input interface was presented in Figure 4.

Fig.4. Interface of the contaminated site information input for remediation technology selection decision.

The next step was to give the score of all kinds of technical alternatives for each indicator according to the evaluation criteria. The scoring results would be saved in the data base of the system. In our case study, the scores of the 21 alternatives for the 12 indicators were shown in Table 3.

Finally, according to the technical alternative's score and weight for each indicator, the integrated score of each technical alternative was calculated by using the PROMETHEE II approach. In the case mentioned above, after the subsystem accounting, the top 5 alternatives were shown in Table 4.

3.3 Discussion

3.3.1 Effectiveness of the technologies selection results

According to the relationship between the pollution type and the remediation technologies reflected by indicator C10, all of ex-situ and in-situ soil solidification and stabilization, ex-situ and in-situ glass transition, exsitu soil washing and phytoremediation were mature methods to deal with the soil arsenic contamination. But seen from the difficulty for management and control （C4）, interference of the remediation zone （C6） and social benefits （B4）, the advantage of glass transition was not obvious. Moreover, the technical maturity （C3） of in-situ glass transition was lower than ex-situ glass transition, so the in-situ glass transition is not recommended by the technology selection decision-making subsystem. As the yellow-cinnamon soil was not appropriate for soil washing repair. It some extent also affected the composite score of soil washing technology in this case. In addition, the score of phytoremediation technology was higher than both ex-suit soil washing and glass transition under cost control priority condition, mainly due to the cost of excavation and soil transfer could be saved.

Table 3 Evaluation of contaminated soil remediation technologies.

Table 4. AHP-PROMETHEE II analytical result for soil remediation.

The soil solidification and stabilization technology solidify the contamination into the contamination medium to make it stable. It is widely used for heavy metal soil contamination remediation and shows advantage in several kinds of heavy metal contaminations remediation（Ma and Garbers-Craig, 2006）. This technology costs cheaper, so it can be used in some non-sensitivity area to reduce the cost. In America, most super foundation projects about the inorganic contamination used this technology. In China, many heavy metal soil contaminations and the Chromium slag soil contamination remediation also used it. In this case, the soil contamination is caused by the heavy metal arsenic. So the soil solidification and stabilization technology is chosen.

In the present case decisions, regardless of the considering cost control priority or the time control priority of the remediation technology selection, ex-situ, in-site soil solidification and stabilization were the top tworecommended alternatives.

Moreover, in this case the identified contamination plume to remedy is 4.5m； the depth of contamination is 3.5m. It costs cheaper due to a relative small range. So, the higher cost of the ex-situ remediation technology is a better choice than the lower cost of the in-site remediation technology. According to the analysis of decision results for this case, the ex-situ soil solidification and stabilization technology is better than the in-situ soil solidification and stabilization technology.

3.3.2 Evaluation criterion for remediation technology selection decision-making

In the processing of the site contamination remediation decision, the ex-situ remediation technology and the in-situ remediation technology show a lot of differences in cost. For the small range of remediation area, the ex-situ remediation technology is more suitable. But for the large range of remediation area, the in-situ remediation technology is taken into consideration due to the high level cost. However, due to the relationship between the acceptable level of economic cost and the local socioeconomic development, standardization was difficult. In this study, for the calculating of preference indicator Pk（ai,aj） of C1 with Eq.5, p needed to be adjusted in practice. Soil physical and chemical properties can impact on the pollutant migration and transformation directly. In this study, we only consider the soil type influence on the remediation technology selection； however, further researches are needed on the influences of soil vertical distribution and spatial heterogeneity. In addition, it should be noted that the technology maturity related indicators in the decisionmaking process will be changed with the technologies development in the future.

4 Conclusion

An Environmental Decision Support System （EDSS） has been developed based on AHP-PROMETHEE II approach for the purpose of identifying optimum soil remediation approaches for contaminated site. In the EDSS, the hierarchical decision tree was constructed according to 4 criteria, i.e., time and economic costs, technical levels, technical applicability and social benefits, 12 indicators, and 21 soil remediation alternatives. Specifically, the inputs, outputs and workflow were presented in detail with a soil remediation decision for a suppositional site arsenic pollution case. In general, the methodology proposed here could meet the contaminated site remediation decision requirements of quickness and effectiveness with relatively less site information. We also discussed the effectiveness and shortcomings of the proposed methodology. As to decision support of soil remediation technology selection, improvements should be made in scoring judgment according to the technological development.

Acknowledgment

This work was supported by a Grant from the National High-Tech Research and Development （863） Programme of China （No. 2009AA06A41803）. The authors appreciate the helpful comments given by editors and anonymous reviewers over this paper.

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Email address： hjzhx@mail.buct.edu.cn, Tel.： +86-010-64444924, Fax： +86-010-64413170.

ISSN 2325-6192, eISSN 2325-6206/$- see front materials ? 2012 L&H Scientific Publishing, LLC. All rights reserved.

10.5890/JEAM.2015.3.005

Accepted 22 March 2015

Available online 1 October 2015