1. Jorquera H, Montoya LD, Rojas NY. Urban Air Pollution. In: Urban Climates in Latin America: Springer International Publishing; 2019, pp. 137–65.
2. Chen M, Yang J, Hu L, Hossain MS, Muhammad G. urban healthcare big data system based on crowdsourced and cloud-based air quality indicators. IEEE Communications Magazine 2018; 56(11): 14–20.
3. Bai Y, Li Y, Wang X, Xie J, Li C. Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions. Atmos Pollut Res 2016; 7(3): 557–66.
4. Cheng W, Shen Y, Zhu Y, Huang L. A neural attention model for urban air quality inference: Learning the weights of monitoring stations. Available from: URL: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16607/15925.
5. Gantt B, Meskhidze N, Zhang Y, Xu J. The effect of marine isoprene emissions on secondary organic aerosol and ozone formation in the coastal United States. Atmos Environ 2010; 44(1): 115–21.
6. Urbanski SP, Hao WM, Nordgren B. The wildland fire emission inventory: Western United States emission estimates and an evaluation of uncertainty. Atmospheric Chemistry and Physics 2011; 11(24): 12973–3000.
7. Gao XL, Hu TJ, Wang K. Research on motor vehicle exhaust pollution monitoring technology. Applied Mechanics and Materials 2014; 620: 244–7.
8. Wang H, Xing C, Yu F. Study of the hydrological time series similarity search based on Daubechies wavelet transform. Unifying Electrical Engineering and Electronics Engineering. Lecture Notes in Electrical Engineering 2014, vol 238. Springer, New York, NY.
9. Wang J, Wang Y, Liu H, Yang Y, Zhang X, Li Y, et al. Diagnostic identification of the impact of meteorological conditions on PM2.5 concentrations in Beijing. Atmos Environ 2013; 81: 158–65.
10. Wu Q, Xu W, Shi A, Li Y, Zhao X, Wang Z, et al. Air quality forecast of PM10 in Beijing with Community Multi-scale Air Quality Modeling (CMAQ) system: Emission and improvement. Geosci Model Dev 2014; 7(5): 2243–59.
11. Ozel G, Cakmakyapan S. A new approach to the prediction of PM10 concentrations in Central Anatolia Region, Turkey. Atmos Pollut Res 2015; 6(5): 735–41.
12. Djalalova I, Delle Monache L, Wilczak J. PM2.5 analog forecast and Kalman filter post-processing for the Community Multiscale Air Quality (CMAQ) model. Atmos Environ 2015; 108: 76–87.
13. Saide PE, Carmichael GR, Spak SN, Gallardo L, Osses AE, Mena-Carrasco MA, et al. Forecasting urban PM10 and PM2.5 pollution episodes in very stable nocturnal conditions and complex terrain using WRF–Chem CO tracer model. Atmos Environ 2011; 45(16): 2769–80.
14. Domańska D, Wojtylak M. Application of fuzzy time series models for forecasting pollution concentrations. Expert Syst Appl 2012; 39(9): 7673–9.
15. Yahya K, Zhang Y, Vukovich JM. Real-time air quality forecasting over the southeastern United States using WRF/Chem-MADRID: Multiple-year assessment and sensitivity studies. Atmos Environ 2014; 92: 318–38.
16. Feng Y, Zhang W, Sun D, Zhang L. Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification. Atmos Environ 2011; 45(11): 1979–85.
17. Wu S, Feng Q, Du Y, Li X. Artificial neural network models for daily PM10 air pollution index prediction in the urban area of Wuhan, China. Environ Eng Sci 2011; 28(5): 357–63.
18. Paschalidou AK, Karakitsios S, Kleanthous S, Kassomenos PA. Forecasting hourly PM10 concentration in Cyprus through artificial neural networks and multiple regression models: Implications to local environmental management. Environ. Sci Pollut Res 2011; 18(2): 316–27.
19. Antanasijević DZ, Ristić MĐ, Perić-Grujić AA, Pocajt VV. Forecasting human exposure to PM10 at the national level using an artificial neural network approach. J Chemom 2013; 27(6): 170–7.
20. Li C, Liang M, Wang T. Criterion fusion for spectral segmentation and its application to optimal demodulation of bearing vibration signals. Mech Syst Signal Pr 2015; 64-65: 132–48.
21. Mohebbi MR, Jashni AK, Dehghani M, Hadad K. Short-Term prediction of carbon monoxide concentration using artificial neural network (NARX) without traffic data: Case study: Shiraz City. Iran J Sci Technol Trans Civ Eng 2019; 43: 533-40.
22. Gao M, Yin L, Ning J. Artificial neural network model for ozone concentration estimation and Monte Carlo analysis. Atmospheric Environment 2018; 184: 129–39.
23. Kalvandi R, SafiKhani K, Najafi Gh, Babakhanlou P. Identification of medicinal plants of Hamedan Province. Iranian Journal of Medicinal and Aromatic Plants 2007; 23(3): 350-74. [In Persian]
24. Ostad-Ali-Askari K, Shayannejad M, Ghorbanizadeh-Kharazi H. Artificial neural network for modeling nitrate pollution of groundwater in marginal area of Zayandeh-rood River, Isfahan, Iran. KSCE J Civ Eng 2017; 21(1): 134–40.
25. Parsimehr M, Shayesteh K, Godini K, Bayat Varkeshi M. Using multilayer perceptron artificial neural network for predicting and modeling the chemical oxygen demand of the Gamasiab River. Avicenna J Environ Health Eng 2018; 5(1): 15–20.
26. Asadpour G, Nasrabadi T. Municipal and medical solid waste management in different districts of Tehran, Iran. Fresenius Environ Bull 2011; 20(12): 3241–5.
27. Aziz K, Rahman A, Shamseldin A Y, Shoaib M. Co-active neuro fuzzy inference system for regional flood estimation in Australia. J Hydrol Environ Res 2013; 1(1): 11–20.
28. Malik A, Kumar A, Kisi O. Monthly pan-evaporation estimation in Indian central Himalayas using different heuristic approaches and climate based models. Computers and Electronics in Agriculture 2017; 143: 302–13.
29. Aytek A. Co-active neurofuzzy inference system for evapotranspiration modeling. Soft Computing 2009; 13(7): 691-700.