Air Quality Index and Public Health: Modelling Using Fuzzy Inference System
[1]
Bouharati Saddek, Laboratory of Intelligent Systems, Faculty of Technology, UFAS Setif1 University, Algeria; Faculty of Natural Sciences and Life, UFAS Setif1 University, Algeria.
[2]
Benzidane Chahra, Faculty of Natural Sciences and Life, UFAS Setif1 University, Algeria.
[3]
Braham-Chaouch Wafa, Research Unit Renewable Saharan Environments (URER. MS), Adrar, Algeria.
[4]
Boumaïza Souad, Faculty of Natural Sciences and Life, UFAS Setif1 University, Algeria.
The Air Quality Index (AQI) is divided into some categories indicating increasing levels of health concern. An AQI value over certain threshold represents hazardous air quality whereas, if it is below a certain value, the air quality is good. Each country or continent established its standards and limits as color code corresponding to a range of index values (very low, low, medium, high and very high). Each color matches the effect of air pollution on a category of sensitive population when this pollution is likely to be affected. The problem is that the limits on these index values are sharp and characterized by their uncertainty and imprecision. For the effect on a population group is very complex to predict accurately. It depends from one person to another even it belongs to the same category of classification. It is not that from a unit value index to switch from one color to another such category of people will be affected and the other is no longer relevant. In this study, we find that the transition between ranges colors normally is fuzzy due to their incertitude effect on levels of health population concern. We found it useful to have analytical techniques based on artificial intelligence, especially the principles of fuzzy logic tool. The use of the Fuzzy inference system, demonstrate his capability for addressing the complex problems of uncertainty data. The FIS model was structured to prevent the nature of risk disease according the AQI values in inputs of system.
Air Pollution, Air Quality, Artificial Intelligence, Fuzzy Logic
[1]
McCollister, G., and Wilson, K. 1975. Linear stochastic models for forecasting daily maxima and hourly concentrations of air pollutants. Atmos. Environ., 9, 417–423.
[2]
Aron, R., and Aron, I. 1978. Statistical forecasting models: Carbon monoxide concentrations in the Los Angeles basin. J. Air Pollut. Control Assoc., 28, 681–684.
[3]
Wolff, G., and Lioy, P. 1978. An empirical model for forecasting maximum daily ozone levels in the northeastern U.S. J. Air Pollut. Control Assoc., 28, 1035–1038.
[4]
Lin, Y., 1982. Oxidant prediction by discriminate analysis in the South coast air basin of California. Atmos. Environ., 16, 135–143.
[5]
Robeson, S., and Steyn, D. 1990. Evaluation and comparison of statistical forecast models for daily maximum ozone concentrations. Atmos. Environ., 24B, 303–312.
[6]
Brian, E. and al. 2010. Using National Air Quality Forecast Guidance to Develop Local Air Quality Index Forecasts American Meteorological Society. 313-326
[7]
Hubbard, M., and Cobourn, W.1998. Development of a regression model to forecast ground-level ozone concentrations in Jefferson County, Kentucky. Atmos. Environ., 32, 2637–2647.
[8]
Gaza, R. 1998. Mesoscale meteorology and high ozone in the northeast United States. J. Appl. Meteor., 37, 961–967.
[9]
Davis, J., and Speckman, P. 1999. A model for predicting maximum and 8 h average ozone in Houston. Atmos. Environ., 33, 2487–2500.
[10]
Ryan, W. 1995. Forecasting severe ozone episodes in the Baltimore metropolitan area. Atmos. Environ., 29, 2387–2398.
[11]
Ryan, W., Piety, R. and Luebehusen, E. 2000. Air quality forecasts in the Mid-Atlantic region: Current practice and benchmark skill. Wea. Forecasting, 15, 46–60.
[12]
US EPA. 9 December 2011. Retrieved 8 August 2012.
[13]
Demir, F. Korkmaz, K. 2008. Prediction of lower and upper bounds of elastic modulus of high strength concrete, Constr. Build Mater 22 1385-1393.
[14]
Inan, G., Göktepe, A.B., Ramyar, K., Sezer, A. 2007. Prediction of sulfate expansion of PC mortar using adaptive neurofuzzy methodology, Build Environ. 1264- 1269. 42.
[15]
Abed-Cheniti, K., Dekhili, M., Bouharati, S. 2013. Morphological Characterization of Three Legumes (Vicia spp.) in the Semi-Arid Region of Setif-Algeria using Fuzzy Logic Inference System. International Journal of Science and Engineering Investigations Vol. 2, issue 12, 95-99.
[16]
Allag, F. Zegadi, R. Bouharati, S. Tedjar, L. Bouharati, I. 2013. Dynamic of air pollution and its effect on newborns: Analysis using fuzzy logic inference system. Wulfenia Journal, part no. 2. Pp. 18-25.
