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. 2016 Feb;45(2):203-13.

Towards the Application of Fuzzy Logic for Developing a Novel Indoor Air Quality Index (FIAQI)

Allahbakhsh Javid  1 , Amir Abbas Hamedian  2 , Hamed Gharibi  1 , Mohammad Hossein Sowlat  3
Affiliations

Towards the Application of Fuzzy Logic for Developing a Novel Indoor Air Quality Index (FIAQI)

Allahbakhsh Javid et al. Iran J Public Health. 2016 Feb.

Abstract

Background: In the past few decades, Indoor Air Pollution (IAP) has become a primary concern to the point. It is increasingly believed to be of equal or greater importance to human health compared to ambient air. However, due to the lack of comprehensive indices for the integrated assessment of indoor air quality (IAQ), we aimed to develop a novel, Fuzzy-Based Indoor Air Quality Index (FIAQI) to bridge the existing gap in this area.

Methods: We based our index on fuzzy logic, which enables us to overcome the limitations of traditional methods applied to develop environmental quality indices. Fifteen parameters, including the criteria air pollutants, volatile organic compounds, and bioaerosols were included in the FIAQI due mainly to their significant health effects. Weighting factors were assigned to the parameters based on the medical evidence available in the literature on their health effects. The final FIAQI consisted of 108 rules. In order to demonstrate the performance of the index, data were intentionally generated to cover a variety of quality levels. In addition, a sensitivity analysis was conducted to assess the validity of the index.

Results: The FIAQI tends to be a comprehensive tool to classify IAQ and produce accurate results.

Conclusion: It seems useful and reliable to be considered by authorities to assess IAQ environments.

Keywords: Fuzzy-based indoor air quality index (FIAQI); Indoor air pollution (IAP); Indoor air quality (IAQ).

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Figures

Fig. 1:
Fig. 1:
Weighting assignment to different parameters and groups included in the FIAQI
Fig. 2:
Fig. 2:
Algorithm of the FIAQI index proposed by the present study
Fig. 3:
Fig. 3:
An example of the trapezoidal membership functions for classifying PM10 concentrations
Fig. 4:
Fig. 4:
The surface graph of CO and Ozone indicating the relationships and interactions among the parameters included in the FIAQI
Fig. 5:
Fig. 5:
The outputs of the FIAQI for the indoor air quality of the virtually generated data for the indoor environments
Fig. 6:
Fig. 6:
Correlation between the outputs of the USEPA AQI and those of the FIAQI
See this image and copyright information in PMC

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