Abstract
Air pollution is a common problem in areas with high population density such as big cities. The mega city of Tehran which is capital city of Iran is suffering from poor air quality. In Tehran, Traditional air quality assessment is realized using air quality indices which are determined as max values of selected air pollutants which is mostly base on PM2.5. Thus, air quality assessment depends on strictly describe without taking into account specific other Environmental parameters. In this paper, To demonstrate the application, common air pollutants like CO, O3, NO2, SO2, PM10 and PM2.5 are used as air pollutant parameters, also we were studied over an 2-year period (2015–2017) on daily data of the air quality index (AQI) in Tehran. The artificial intelligence based on neural network and fuzzy inferences methods allows assessing air quality parameters, providing a partial solution to this problem. Accordingly, this study proposes two fuzzy logic system for assessing accurate air quality evaluations, also proposed Seven score stages: Good, Moderate, Unhealthy for Sensitive Group, Unhealthy, Very Unhealthy, Hazardous for evaluating air quality index. Our experimental results show a good performance of the proposed air quality index against other system that those in literature.
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References
Olvera-García, M.Á., Carbajal-Hernández, J.J., Sánchez-Fernández, L.P., Hernández-Bautista, I.: Air quality assessment using a weighted fuzzy inference system. Ecol. Inf. 33, 57–74 (2016)
Carbajal-Hernández, J.J., Sánchez-Fernández, L.P., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F.: Assessment and prediction of air quality using fuzzy logic and autoregressive models. Atmos. Environ. 60, 37–50 (2012)
Aquino, R., de Oliveira, N.F., Barreto, M.L.: Impact of the family health program on infant mortality in Brazilian municipalities. Am. J. Public Health 99(1), 87–93 (2009)
Salazar-Ruiz, E., Ordieres, J., Vergara, E., Capuz-Rizo, S.F.: Development and comparative analysis of tropospheric ozone prediction models using linear and artificial intelligence-based models in mexicali, baja California (Mexico) and Calexico, California (US). Environmental Modelling & Software 23(8), 1056–1069 (2008)
Wang, W., Men, C., Lu, W.: Online prediction model based on support vector machine. Neurocomputing 71(4), 550–558 (2008)
Liu, K.F., Liang, H., Yeh, K., Chen, C.: A qualitative decision support for environmental impact assessment using fuzzy logic. J. Environ. Inform. 13(2), 93–103 (2009)
Mishra, D., Goyal, P.: Neuro-fuzzy approach to forecast NO2 pollutants addressed to air quality dispersion model over Delhi, India. Aerosol Air Qual. Res. 16, 166–174 (2016)
Upadhyay, A., Kanchan, P.G., Yerramilli, A., Gorai, A.K.: Development of fuzzy pattern recognition model for air quality assessment of howrah city. Aerosol Air Qual. Res. 14, 1639–1652 (2014)
Zarandi, M.F., Kalhori, M.R.N., Jahromi, M.: Possibilistic c-means clustering using fuzzy relations. In: 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), pp. 1137–1142. IEEE (2013)
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Abdolkarimzadeh, L., Azadpour, M., Zarandi, M.H.F. (2018). Two Hybrid Expert System for Diagnosis Air Quality Index (AQI). In: Melin, P., Castillo, O., Kacprzyk, J., Reformat, M., Melek, W. (eds) Fuzzy Logic in Intelligent System Design. NAFIPS 2017. Advances in Intelligent Systems and Computing, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-319-67137-6_36
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DOI: https://doi.org/10.1007/978-3-319-67137-6_36
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