Skip to main content
SpringerLink
Log in

Environmental Integrated Assessment via Monte Carlo Simulation with a Case Study of the Mid-Atlantic Region, USA

  • Published:
Environmental Management Aims and scope Submit manuscript

Abstract

Environmental integrated assessments are often carried out via the aggregation of a set of environmental indicators. Aggregated indices derived from the same data set can differ substantially depending upon how the indicators are weighted and aggregated, which is often a subjective matter. This article presents a method of generating aggregated environmental indices in an objective manner via Monte Carlo simulation. Rankings derived from the aggregated indices within and between three Monte Carlo simulations were used to evaluate the overall environmental condition of the study area. Other insights, such as the distribution of good or bad values of indicators at a watershed and/or a subregion, were observed in the study.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Alcamo J (1994) IMAGE 2.0: integrated modeling of global climate change. Kluwer, Dordrecht

    Google Scholar 

  • Andreasen JK, O’Neill RV, Noss R, Slosser NC (2001) Considerations for the development of a terrestrial index of ecological integrity. Ecological Indicators 1(1):21–35

    Article  Google Scholar 

  • Bartell SM, Gardner RH, O’Neill RV, Giddings JM (1983) Error analysis of predicted fates of anthracene in a simulated pond. Environmental Toxicology and Chemistry 2:19–28

    Article  CAS  Google Scholar 

  • Bartell SM, Gardner RH, O’Neill RV (1992) Ecological risk estimation. Lewis, Ann Arbor

    Google Scholar 

  • Binder C, Boumans RM, Costanza R (2003) Applying the Patuxent Landscape Unit Model to human dominated ecosystems: the case of agriculture. Ecological Modelling 159(2–3):161–177

    Article  Google Scholar 

  • Bossel H (1999) Indictors for sustainable development: theory, method, applications. International Institute for Sustainable Development, Manitoba

    Google Scholar 

  • Boughton DA, Smith ER, O’Neill RV (1999) Regional vulnerability: a conceptual framework. Ecosystem Health 5:312–322

    Article  Google Scholar 

  • Boumans RM, Villa F, Costanza R, Voinov A, Voinov H, Maxwell T (2001) Non-spatial calibrations of a general unit model for ecosystem simulations. Ecological Modelling 146(1–3):17–32

    Article  Google Scholar 

  • Cornforth IS (1999) Selecting indicators for assessing sustainable land management. Journal of Environmental Management 56:173–179

    Article  Google Scholar 

  • Dale VH, Beyeler SC (2001) Challenges in the development and use of ecological indicators. Ecological Indicators 1:3–10

    Article  Google Scholar 

  • Davis WS, Simon TP (eds) (1995) Biological assessment and criteria: tools for water resource planning and decision making. Lewis Publishers, Boca Raton

    Google Scholar 

  • Diaz-Balteiro L, Romero C (2004) In search of a natural systems sustainability index. Ecological Economics 49:401–405

    Article  Google Scholar 

  • Dowlatabadi H (1995) Integrated assessment models of climate change: an incomplete overview. Energy Policy 23(3/4):289–296

    Article  Google Scholar 

  • Fishman GS (1996) Monte Carlo: concepts, algorithms, and applications. Springer-Verlag, New York, 728 pp

    Google Scholar 

  • Gardner RH, O’Neill RV (1983) Parameter uncertainty and model predictions: a review of Monte Carlo results. In: Beck MB, Van Straten G (eds) Uncertainty and forecasting of water quality. Springer Verlag, New York, pp 245–257

    Google Scholar 

  • Gardner RH, O’Neill RV, Mankin JB, Carney JH (1981) A comparison of sensitivity analysis and error analysis based on a stream ecosystem model. Ecological Modelling 12:173−190

    Article  Google Scholar 

  • Hanley N, Moffatt I, Faichney R, Wilson M (1999) Measuring sustainability: a time series of alternative indicators for Scotland. Ecological Economics 28:55–73

    Article  Google Scholar 

  • Hardi P, Zdan TJ (1997) Assessing sustainable development: principles and practices. International Institute for Sustainable Development, Manitoba

    Google Scholar 

  • Huff DD, O’Neill RV, Emanuel WR, Elwood JW, Newbold JD (1982) Flow variability and hillslope hydrology. Earth Surfaces Processes and Landforms 7:91–94

    Article  Google Scholar 

  • Jones KB, Riitters KH, Wickham JD, Tankersley RD, O’Neill RV, Chaloud DJ, Smith ER, Neale AC (1997) An ecological assessment of the United States: Mid-Atlantic region: a landscape Atlas. EPA/600/R-97/130. Office of Research and Development, Washington, DC

  • Kalos MH, Whitlock PA (2008) Monte Carlo methods. Wiley-Blackwell, Weinheim, 208 pp

    Book  Google Scholar 

  • Kurtz JC, Jackson LE, Fisher WS (2001) Strategies for evaluating indicators based on guidelines from the Environmental Protection Agency’s Office of Research and Development. Ecological Indicators 1:49–60

    Article  CAS  Google Scholar 

  • Lee N (2006) Bridging the gap between theory and practice in integrated assessment. Environmental Impact Assessment Review 26:57–78

    Article  Google Scholar 

  • Miller GA (1956) The magical number seven plus or minus two: some limits on our capacity for processing information. The Psychological Review 63:81–97

    Article  CAS  Google Scholar 

  • Muxika I, Borja A, Bonne W (2005) The suitability of the marine biotic index (AMBI) to new impact sources along European coasts. Ecological Indicators 5(1):19–31

    Article  Google Scholar 

  • O’Neill RV (1979) Natural variability as a source of error in model predictions. In: Innis GS, O’Neill RV (eds) Systems analysis of ecosystems. International Cooperative Publishing House, Fairland, pp 23–32

    Google Scholar 

  • O’Neill RV, Gardner RH, Hoffman FO, Schwarz G (1981) Parameter uncertainty and estimated radiological dose to man from atmospheric 131l releases: a Monte Carlo approach. Health Physics 40:760–764

    Google Scholar 

  • Pannell DJ, Glenn NA (2000) A framework for the economic evaluation and selection of sustainability indicators in agriculture. Ecological Economics 33(1):135–149

    Article  Google Scholar 

  • Pielou EC (1984) The interpretation of ecological data. Wiley-Interscience, New York, 263 pp

    Google Scholar 

  • Rennings K, Wiggering H (1997) Steps towards indicators of sustainable development: linking economic and ecological concepts. Ecological Economics 20(1):25–36

    Article  Google Scholar 

  • Rice J (2003) Environmental health indicators. Ocean & Coastal Management 46:235–259

    Article  Google Scholar 

  • Rice JC, Rochet M-J (2005) A framework for selecting a suite of indicators for fisheries management. ICES Journal of Marine Science 62:516–527

    Article  Google Scholar 

  • Riitters KH, O’Neill RV, Jones KB (1997) Assessing habitat suitability at multiple scales: a landscape-level approach. Biological Conservation 1997:191–202

    Article  Google Scholar 

  • Rotmans J (1998) Methods for IA: the challenges and opportunities ahead. Environmental Modeling and Assessment 3:155–179

    Article  Google Scholar 

  • Rubinstein RY (1981) Simulation and the Monte Carlo method. John Wiley & Sons, New York, 304 pp

    Book  Google Scholar 

  • Saaty TL (1980) The analytic hierarchy process, planning, priority setting, and resource allocation. McGraw-Hill, New York, 287 pp

    Google Scholar 

  • Smith ER, Tran LT, O’Neill RV (2003) Regional vulnerability assessment for the Mid-Atlantic region: evaluation of integration methods and assessment results. The U.S. Environmental Protection Agency, Technical Report: EPA/600/R-03/082, October 2003

  • Tol RSJ, Vellinga P (1998) The European forum on integrated environmental assessment. Environmental Modeling and Assessment 3:181–191

    Article  Google Scholar 

  • Toth FL, Hizsnyik E (1998) Integrated environmental assessment methods: evolution and applications. Environmental Modeling and Assessment 3:193–207

    Article  Google Scholar 

  • Tran LT, Knight CG, O’Neill RV, Smith ER, Riitters KH, Wickham J (2002) Fuzzy decision analysis for integrated environmental vulnerability assessment of the Mid-Atlantic region. Environmental Management 29:845–859

    Article  Google Scholar 

  • Tran LT, Knight CG, O’Neill RV, Smith ER, O’Connell M (2003) Self-organizing maps for integrated environmental assessment of the U.S. Mid-Atlantic region. Environmental Management 31(6):822–835

    Article  Google Scholar 

  • Tran LT, Knight CG, O’Neill RV, Smith ER (2004) Integrated environmental assessment of the Mid-Atlantic region with analytical network process. Environmental Monitoring and Assessment 94(1/3):263–277

    Article  Google Scholar 

  • Voinov A, Fitz C, Boumans RM, Costanza R (2004) Modular ecosystem modeling. Environmental Modelling & Software 19(3):285–304

    Article  Google Scholar 

  • Wickham JD, Jones KB, Riitters KH, O’Neill RV, Tankersley RD, Smith ER, Neale AC, Chaloud DJ (1999) An integrated environmental assessment of the Mid-Atlantic Region. Environmental Management 24(4):553–560

    Article  Google Scholar 

  • Wilkinson D, Fergusson M, Bowyer C (2004) Sustainable development in the European Commission’s integrated impact assessments for 2003. Institute for European Environmental Policy, London

    Google Scholar 

  • Zalidis GC, Tsiafouli MA, Takavakoglou V, Bilas G, Misopolinos N (2004) Selecting agri-environmental indicators to facilitate monitoring and assessment of EU agri-environmental measures effectiveness. Journal of Environmental Management 70:315–321

    Article  Google Scholar 

Download references

Acknowledgment

The first author gratefully acknowledges partial support from the U.S. Environmental Protection Agency via contract R011038132. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the U.S. Environmental Protection Agency.

Author information

Authors and Affiliations

  1. Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN, 37996-0925, USA

    Liem T. Tran

  2. T N & Associates, Oak Ridge, TN, USA

    Robert V. O’Neill

  3. U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, Research Triangle Park, Durham, NC, USA

    Elizabeth R. Smith

Authors
  1. Liem T. Tran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Robert V. O’Neill
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elizabeth R. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liem T. Tran.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tran, L.T., O’Neill, R.V. & Smith, E.R. Environmental Integrated Assessment via Monte Carlo Simulation with a Case Study of the Mid-Atlantic Region, USA. Environmental Management 44, 387–393 (2009). https://doi.org/10.1007/s00267-009-9326-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00267-009-9326-4

Keywords

Search

Navigation