M. Kampa and E. Castanas, Human health effects of air pollution, Environmental Pollution, vol.151, issue.2, pp.362-367, 2008.
DOI : 10.1016/j.envpol.2007.06.012

G. Qin and Z. Meng, Effects of sulfur dioxide derivatives on expression of oncogenes and tumor suppressor genes in human bronchial epithelial cells, Food and Chemical Toxicology, vol.47, issue.4, pp.734-744, 2009.
DOI : 10.1016/j.fct.2009.01.005

K. R. Baker and K. M. Foley, A nonlinear regression model estimating single source concentrations of primary and secondarily formed PM2, Atmos. Environ, vol.5, issue.45, pp.3758-3767, 2011.

A. Nebot and F. Mugica, Small-Particle Pollution Modeling Using Fuzzy Approaches, Simulation and Modeling Methodologies, Technologies and Applications , Advances in Intelligent Systems and Computing, pp.239-252, 2014.
DOI : 10.1007/978-3-319-03581-9_17

M. Oprea, E. G. Dragomir, S. F. Mihalache, and M. Popescu, Prediction methods and techniques for PM2.5 concentration in urban environment (in Romanian) Methods to assess the effects of air pollution with particulate matter on children's health, pp.387-428, 2014.

N. Kumar, A. Chu, and A. Foster, An empirical relationship between PM2.5 and aerosol optical depth in Delhi Metropolitan, Atmospheric Environment, vol.41, issue.21, pp.4492-4503, 2007.
DOI : 10.1016/j.atmosenv.2007.01.046

A. Akkoyunlu, K. Yetilmezsoy, F. Erturk, and E. Oztemel, A neural network-based approach for the prediction of urban SO2 concentrations in the Istanbul Metropolitan Area, Inter, J. Environ. Pol, vol.40, pp.301-321, 2010.

I. Yilmaz and O. Kaynar, Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils, Expert Systems with Appl, pp.5958-5966, 2011.

F. C. Morabito and M. Versaci, Fuzzy neural identification and forecasting techniques to process experimental urban air pollution data, Neural Networks, vol.16, issue.3-4, pp.493-506, 2003.
DOI : 10.1016/S0893-6080(03)00019-4

Y. Yildirim and M. Bayramoglu, Adaptive neuro-fuzzy based modelling for prediction of air pollution daily levels in city of Zonguldak, Chemosphere, vol.63, issue.9, pp.1575-1582, 2006.
DOI : 10.1016/j.chemosphere.2005.08.070

M. Ashish and B. Rashmi, Prediction of daily air pollution using wavelet decomposition and adaptive-network-based fuzzy inference system, International Journal of Environmental Sciences, vol.2, issue.1, pp.185-196, 2011.

S. Haykin, Neural networks. A comprehensive foundation, 1999.

K. Hornik, Approximation capabilities of multilayer feedforward networks, Neural Networks, vol.4, issue.2, pp.251-257, 1991.
DOI : 10.1016/0893-6080(91)90009-T

A. Kurt and A. B. Oktay, Forecasting air pollutant indicator levels with geographic models 3 days in advance using neural networks, Expert Systems with Appl, pp.7986-7992, 2010.

H. J. Fernando, . M. Mammarella, . G. Grandoni, P. Fedele, D. Marco et al., Forecasting PM10 in metropolitan areas: Efficacy of neural networks, PM10 in metropolitan areas. Efficacy of neural networks, pp.62-67, 2012.
DOI : 10.1016/j.envpol.2011.12.018

X. Feng, Q. Li, Y. Zhu, J. Hou, L. Jin et al., Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation, Atmospheric Environment, vol.107, pp.118-128, 2015.
DOI : 10.1016/j.atmosenv.2015.02.030

M. Oprea, S. F. Mihalache, and M. Popescu, A comparative study of computational intelligence techniques applied to PM2.5 air pollution forecasting, 2016 6th International Conference on Computers Communications and Control (ICCCC), pp.103-108, 2016.
DOI : 10.1109/ICCCC.2016.7496746