IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i1p134-d88118.html
   My bibliography  Save this article

A Hybrid Fuzzy Inference System Based on Dispersion Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning

Author

Listed:
  • Behnam Tashayo

    (Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967 15433, Iran)

  • Abbas Alimohammadi

    (Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967 15433, Iran
    Center of Excellence in Geospatial Information Technology, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967 15433, Iran)

  • Mohammad Sharif

    (Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967 15433, Iran)

Abstract

Characterizing the spatial variation of traffic-related air pollution has been and is a long-standing challenge in quantitative environmental health impact assessment of urban transportation planning. Advanced approaches are required for modeling complex relationships among traffic, air pollution, and adverse health outcomes by considering uncertainties in the available data. A new hybrid fuzzy model is developed and implemented through hierarchical fuzzy inference system (HFIS). This model is integrated with a dispersion model in order to model the effect of transportation system on the PM 2.5 concentration. An improved health metric is developed as well based on a HFIS to model the impact of traffic-related PM 2.5 on health. Two solutions are applied to improve the performance of both the models: the topologies of HFISs are selected according to the problem and used variables, membership functions, and rule set are determined through learning in a simultaneous manner. The capabilities of this proposed approach is examined by assessing the impacts of three traffic scenarios involved in air pollution in the city of Isfahan, Iran, and the model accuracy compared to the results of available models from literature. The advantages here are modeling the spatial variation of PM 2.5 with high resolution, appropriate processing requirements, and considering the interaction between emissions and meteorological processes. These models are capable of using the available qualitative and uncertain data. These models are of appropriate accuracy, and can provide better understanding of the phenomena in addition to assess the impact of each parameter for the planners.

Suggested Citation

  • Behnam Tashayo & Abbas Alimohammadi & Mohammad Sharif, 2017. "A Hybrid Fuzzy Inference System Based on Dispersion Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning," Sustainability, MDPI, vol. 9(1), pages 1-21, January.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:1:p:134-:d:88118
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/1/134/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/9/1/134/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Grazia Ghermandi & Sara Fabbi & Barbara Arvani & Giorgio Veratti & Alessandro Bigi & Sergio Teggi, 2017. "Impact Assessment of Pollutant Emissions in the Atmosphere from a Power Plant over a Complex Terrain and under Unsteady Winds," Sustainability, MDPI, vol. 9(11), pages 1-16, November.
    2. Meelan Thondoo & David Rojas-Rueda & Joyeeta Gupta & Daniel H. de Vries & Mark J. Nieuwenhuijsen, 2019. "Systematic Literature Review of Health Impact Assessments in Low and Middle-Income Countries," IJERPH, MDPI, vol. 16(11), pages 1-21, June.
    3. Arezoo Mokhtari & Behnam Tashayo & Kaveh Deilami, 2021. "Implications of Nonstationary Effect on Geographically Weighted Total Least Squares Regression for PM 2.5 Estimation," IJERPH, MDPI, vol. 18(13), pages 1-17, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:9:y:2017:i:1:p:134-:d:88118. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.