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Development of case historical logical air quality indices via fuzzy mathematics (Mamdani and Takagi–Sugeno systems), a case study for Shahre Rey Town

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Abstract

In the present world, various natural and human activities introducing contaminants to the environment system result in diminishing of air quality in both global and local scopes. In the considered scopes, environmental officials and corresponding societies must be informed of degree of air quality. As a result, many scientists and standards try to develop and present a variety of air quality indices for estimation of adverse effects of air pollution, though the indices have their own limitations such as high levels of subjectivity and not hybrid attitude. This study attempts to develop fuzzy-based air quality indices analyzing: CO, PM2.5, SO2, NO2 and O3 for most urban areas or industrial areas without special pollutants like BTEX or H2S. Two fuzzy inference systems with different types: 1—Mamdani and 2—zero-order Takagi–Sugeno, are prepared for assessing the air quality index. In Mamdani Fuzzy Air Quality Index (MFAQI) different weighting factors are applied to each pollutant to include their degree of significance based on a query analyzing the health impacts, health precautions and safety measures. Next, the Takagi–Sugeno Fuzzy Air Quality Index (TSFAQI) is produced by mam2sug code in MATLAB R2013a. The naming FAQIs is applied for Shahre Rey Town as a case study to have a measure of applicability and performance of the proposed fuzzy indices. The concentration data for air criteria pollutants relate to the 2-year interval from April 2013 to April 2015. The prepared MFAQI and TSFAQI are studied and compared to the well-known air quality index (AQI) by United States Environmental Protection Agency for cross-validations. The cross-validation functioned by CF tool in MATLAB R2013a presents good fittings with slopes of 0.9934 and 1.079 (with 95 % accuracy) relatively for MFAQI and TSFAQI. The results express that the TSFAQI overestimates the AQI, while the MFAQI underestimates the AQI. On the other hand, TSFAQI exhibits less deviation from AQI; this is while the largest deviation occurred in the study equals 14.8 %.

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Notes

  1. Pollutant Standard Index.

  2. Revised Air Quality Index.

  3. Environmental Risk Assessment.

  4. Environmental Quality Assessment.

  5. Environmental Impact Assessment.

  6. Benzene, Toluene, Ethyl Benzene, Xylene.

  7. Fuzzy Air Quality Index.

  8. Analytical Hierarchy Process.

  9. Detrended Fluctuation Analysis.

  10. Air Pollution Indices.

  11. Principal Component Analysis.

  12. Fuzzy c-means.

  13. Indoor air quality.

  14. Total Volatile Organic Compounds.

  15. Fuzzy Logic Assessment System.

  16. Membership Function.

  17. Fuzzy Inference System.

  18. Center of Gravity.

  19. Dose Response Analysis.

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Acknowledgments

The authors are very happy to acknowledge the everlasting invention of professor L., A., Zade as the father of Fuzzy sets. We gratefully wish to thank the scientific teams: Sowlat et al. (2011), Dunea et al. (2011) and Fernandez et al. (2006) for their literatures all of which helped us to edit the present study.

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Authors and Affiliations

  1. Department of Environmental Engineering, College of Environment, UoE, Karaj, Iran

    Hamid Sarkheil & Shahrokh Rahbari

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  1. Hamid Sarkheil
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  2. Shahrokh Rahbari
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Correspondence to Hamid Sarkheil.

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Sarkheil, H., Rahbari, S. Development of case historical logical air quality indices via fuzzy mathematics (Mamdani and Takagi–Sugeno systems), a case study for Shahre Rey Town. Environ Earth Sci 75, 1319 (2016). https://doi.org/10.1007/s12665-016-6131-2

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  • DOI: https://doi.org/10.1007/s12665-016-6131-2

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