Research article

Partial ordering as decision support to evaluate remediation technologies

  • Received: 23 January 2015 Accepted: 17 February 2015 Published: 25 February 2015
  • When facing necessary remediation actions a series of potential technologies may be considered. Typically the eventual selection of the more appropriate remediation technology cannot be made based on a single indicator. Thus, the analysis turns into a multi-criteria decision analysis and an initial step is consequently the development of a multi-indicator system (MIS). A one-dimensional metric serving as an ordering index can easily be obtained by combining the component indicators via aggregation techniques, which unambiguously will lead to loss of information and possibly to more or less severe compensation effects. The present study proposes an alternative to aggregation based on simple concepts of partial order methodology. Hence, for illustrative and explanatory purposes an exemplary MIS corresponding of 5 possible remediation options, ROi, i = 1-5, in addition to the non-remedied situation, RO0, and the complete remediation, ROmax, for 3 chemicals was set up and subsequently analyzed. The results are shown to be distinctly different from an ordering based on an aggregated indicator. In contrast to the total order that is constructed from an aggregated indicator partial ordering allow only for a weak ordering, as e.g., based on average orders. It is shown how the more appropriate remediation technology may be selected and further how the results obtained may serve as a basis for selective improvement of specific remediation options. The methods described here is not limited to, e.g., chemicals but have a more universal applicability.

    Citation: Lars Carlsen. Partial ordering as decision support to evaluate remediation technologies[J]. AIMS Environmental Science, 2015, 2(1): 110-121. doi: 10.3934/environsci.2015.1.110

    Related Papers:

  • When facing necessary remediation actions a series of potential technologies may be considered. Typically the eventual selection of the more appropriate remediation technology cannot be made based on a single indicator. Thus, the analysis turns into a multi-criteria decision analysis and an initial step is consequently the development of a multi-indicator system (MIS). A one-dimensional metric serving as an ordering index can easily be obtained by combining the component indicators via aggregation techniques, which unambiguously will lead to loss of information and possibly to more or less severe compensation effects. The present study proposes an alternative to aggregation based on simple concepts of partial order methodology. Hence, for illustrative and explanatory purposes an exemplary MIS corresponding of 5 possible remediation options, ROi, i = 1-5, in addition to the non-remedied situation, RO0, and the complete remediation, ROmax, for 3 chemicals was set up and subsequently analyzed. The results are shown to be distinctly different from an ordering based on an aggregated indicator. In contrast to the total order that is constructed from an aggregated indicator partial ordering allow only for a weak ordering, as e.g., based on average orders. It is shown how the more appropriate remediation technology may be selected and further how the results obtained may serve as a basis for selective improvement of specific remediation options. The methods described here is not limited to, e.g., chemicals but have a more universal applicability.


    加载中
    [1] Bruggemann R, Carlsen L (2011) An Improved Estimation of Averaged Ranks of Partially Orders. MATCH-Commun Math Comput Chem 65: 383-414.
    [2] Annoni P, Bruggemann R, Carlsen L (2015) A Multidimensional View on Poverty in the European Union by Partial Order Theory. J App Stat 42: 535-554. doi: 10.1080/02664763.2014.978269
    [3] Munda G (2008) Social Multi-Criteria Evaluation for a Sustainable Economy. Springer-Verlag, Berlin.
    [4] Brans JP, Vincke PH (1985) A Preference Ranking Organisation Method (The PROMETHEE Method for Multiple Criteria Decision - Making). Manage Sci 31: 647-656. doi: 10.1287/mnsc.31.6.647
    [5] Roy B (1972) Electre III: Un Algorithme de Classements fonde sur une representation floue des Preferences En Presence de Criteres Multiples. Cahiers du Centre d'Etudes de Recherche Operationelle 20: 32-43.
    [6] Roy B, Bouyssou D (1986) Comparison of two decision-aid models applied to a nuclear power plant siting example. Eur J Oper Res 25: 200-215. doi: 10.1016/0377-2217(86)90086-X
    [7] Colorni A, Paruccini M, Roy B (2001) A-MCD-A, Aide Multi Critere a la Decision, Multiple Criteria Decision Aiding. JRC European Commission, Ispra.
    [8] Figueira J, Greco S, Ehrgott M (2005) Multiple Criteria Decision Analysis, State of the Art Surveys. Springer, Boston.
    [9] Borken J (2005) Umweltindikatoren als ein Instrument der Technikfolgenabschätzung - Selektion, Aggregation und multikriterielle Bewertung am Beispiel des Verkehrs. Fakultät für Angewandte Wissenschaften. Universität Freiburg/Breisgau. PhD-Thesis, pp. 153.
    [10] Bruggemann R, Carlsen L (2012) Multi-criteria decision analyses. Viewing MCDA in terms of both process and aggregation methods: Some thoughts, motivated by the paper of Huang, Keisler and Linkov. Sci Tot Environ 425: 293-295.
    [11] Bruggemann R, Carlsen L (2015) Incomparable—What Now, III. Incomparabilities, Elucidated by a Simple Version of ELECTRE III and a Fuzzy Partial Order Approach. MATCH-Commun Math Comput Chem 73: 277-302.
    [12] Bruggemann, R. and Carlsen, L. (2006) Partial Order in Environmental Sciences and Chemistry. Springer, Berlin.
    [13] Bruggemann R, Patil GP (2011) Ranking and Prioritization for Multi-indicator Systems—Introduction to Partial Order Applications. Springer, New York.
    [14] Bruggemann R, Annoni P (2014) Average heights in partially ordered sets. MATCH-Commun Math Comput Chem 71: 117-142.
    [15] Morton J, Pachter L, Shiu A, et al. (2009) Convex Rank Tests and Semigraphoids. SIAM J Discrete Math 23: 1117-1134. doi: 10.1137/080715822
    [16] Wienand O (2006) lcell http://bio.math.berkeley.edu/ranktests/lcell/ (accessed Feb 2015).
    [17] De Loof K, De Meyer H, De Baets B (2006) Exploiting the Lattice of Ideals Representation of a Poset. Fund Inform 71: 309-321.
    [18] Carlsen L (2015) How synergistic- or antagonistic effects may influence the mutual hazard ranking of chemicals, submitted for publication.
    [19] Bruggemann R, Carlsen L, Voigt K, et al. (2014) PyHasse software for partial order analysis, In: Bruggemann, Carlsen L, and Wittmann, J.eds., Multi-Indicator Systems and Modelling in Partial Order, Springer, New York, 2014, 389-423.
    [20] Sørensen PB, Mogensen BB, Carlsen L, et al. (2000) The influence of partial order ranking from input parameter uncertainty. Definition of a robustness parameter Chemospher41: 595-601.
    [21] Lerche D, Brüggemann R, Sørensen P et al. (2002) A comparison of partial order technique with three methods of multi-criteria analysis for ranking of chemical substances. J Chem Inf Comput Sci 42: 1086-1098. doi: 10.1021/ci010268p
    [22] Lerche D, Sørensen PB, Brüggemann R (2003) Improved estimation of ranking probabilities in partial orders using random linear extensions by approximation of the mutual ranking probability. J Chem Inf Comput Sci 43: 1471-1480. doi: 10.1021/ci0300036
    [23] Carlsen L (2008) Hierarchical partial order ranking. Environ Pollut 155: 247-253. doi: 10.1016/j.envpol.2007.11.023
  • Reader Comments
  • © 2015 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3633) PDF downloads(1057) Cited by(3)

Article outline

Figures and Tables

Figures(3)  /  Tables(6)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog