Characterization of the Concentrations of Volatile Organic Compounds in the Romanian Littoral using General Regression Neural Networks: A Case Study

References

• Alvarez-Hornos, F. J., C. Gabaldon, V. Martinez-Soria, M. Martin, P. Marzal, and J. M. Penya-roja. 2008. Biofiltration of ethylbenzene vapours: Influence of the packing material. Bioresource Technology 99: 269–76. doi:10.1016/j.biortech.2006.12.022
   PubMed Web of Science ®Google Scholar
• Appel, K. W., K. M. Foley, J. O. Bash, R. W. Pinder, R. L. Dennis, D. J. Allen, and K. Pickering. 2011. A multi-resolution assessment of the Community Multiscale Air Quality (CMAQ) model v4.7 wet deposition estimates for 2002–2006. Geoscientific Model Development 4(2): 357–71. doi:10.5194/gmd-4-357-2011
   Web of Science ®Google Scholar
• Barbes, L., A. Barbulescu, C. Rădulescu, C. Stihi, and E. D. Chelărescu. 2014. Determination of heavy metals in leaves and bark of Populus nigra L by atomic absorption spectrometry. Romanian Reports in Physics 66(3): 877–86.
   Web of Science ®Google Scholar
• Barbes, L., C. Neagu, L. Melnic, C. Ilie, and M. Velicu. 2009. The use of artificial neural network (ANN) for prevision of some airborne pollutants concentration in urban areas. Rev. Chim. (Bucuresti). 60(3): 301–07. Available at http://www.revistadechimie.ro/pdf/BARBES%20L...pdf
   Web of Science ®Google Scholar
• Barbulescu, A., and L. Barbes. 2013. Mathematical models for inorganic pollutants in Navodari area, Romania. Rev. Chim. (Bucuresti). 64(7): 747–53. Available at http://www.revistadechimie.ro/pdf/BARBULESCU%20A.pdf%207%2013.pdf
   Web of Science ®Google Scholar
• Barbulescu, A., and L. Barbes. 2014. Models for pollutants’ correlation in the Romanian littoral. Romanian Reports in Physics 66(4): 1189–99.
   Web of Science ®Google Scholar
• Barbulescu, A., and J. Deguenon. 2014a. Change point detection and models for precipitation evolution. Case study. Romanian Journal of Physics 59(5–6): 590–600.
   Web of Science ®Google Scholar
• Barbulescu, A., and J. Deguenon. 2014b. Models for trend of precipitation in Dobrudja. Environmental Engineering and Management Journal 13(4): 873–80.
   Web of Science ®Google Scholar
• Barnett, V., and T. Lewis. 1994. Outliers in statistical data, 3rd ed., 608. New York, NY: Wiley.
   Google Scholar
• Berenjian, A., N. Chan, and H. J. Malmiri. 2012. Volatile organic compounds removal methods: A review. American Journal of Biochemistry and Biotechnology 8(4): 220–29. doi:10.3844/ajbbsp.2012.220.229
   Google Scholar
• Bereznicki, S. D., J. R. Sobus, A. F. Vette, M. A. Stiegel, and R. W. Williams. 2012. Assessing spatial and temporal variability of VOCs and PM-components in outdoor air during the Detroit Exposure and Aerosol Research Study (DEARS). Atmospheric Environment 61: 159–68. doi:10.1016/j.atmosenv.2012.07.008
   Web of Science ®Google Scholar
• Carruthers, D., X. Sheng, and E. Vanvyve. 2008. Regulatory requirements for dispersion modelling in Taiwan including the use of ADMS. 12th International Conference on Harmonisation, Cavtat, Croatia, October 2008: 152–56. Available at http://www.harmo.org/Conferences/Proceedings/_Cavtat/publishedSections/O_S3-02.pdf
   Google Scholar
• Chatfield, C. 2001. Time - series forecasting. Boca Raton, FL: Chapman & Hall/CRC.
   Google Scholar
• De Nevers, N. 2000. Air pollution control engineering, 2nd ed. Boston, MA: McGraw-Hill.
   Google Scholar
• European Environment Agency. 2013. Exceedance of air quality limit values in urban areas (Indicator CSI 004).
   Google Scholar
• European Parliament and Council. 2004. Directive 2004/107/EC of the European Parliament and of the Council of 15 December 2004 relating to arsenic, cadmium, mercury, nickel and polycyclic aromatic hydrocarbons in ambient air, OJL 23, 26.1.2005, pp. 3–16.
   Google Scholar
• European Parliament and Council. 2008. Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe, OJL 11.6.2008.
   Google Scholar
• Grasic, B., P. Mlakar, and M. Z. Boznar. 2006. Ozone prediction based on neural networks and Gaussian processes. Il Nuovo Cimento C 29(6): 651–62. doi: 10.1393/ncc/i2006-10011-5
   Google Scholar
• Hellen, H., S. Hakola, H. Haaparanta, H. Pietarila, and M. Kauhaniemi. 2008. Influence of residential wood combustion on local air quality. The Science of the Total Environment 393: 283–90. doi:10.1016/j.scitotenv.2008.01.019
   PubMed Web of Science ®Google Scholar
• Hrdlickova, Z., J. Michalek, M. Kolar, and V. Vesely. 2008. Identification of factors affecting air pollution by dust aerosol PM10 in Brno City, Czech Republic. Atmospheric Environment 42: 8661–73. doi:10.1016/j.atmosenv.2008.08.017
   Web of Science ®Google Scholar
• Huang, B., C. Lei, C. Wei, and G. Zeng. 2014. Chlorinated volatile organic compounds (Cl-VOCs) in environment – Sources, potential human health impacts, and current remediation technologies. Environment International 71: 118–38. doi:10.1016/j.envint.2014.06.013
   PubMed Web of Science ®Google Scholar
• Kabir, E., and K.-H. Kim. 2010. An on-line analysis of 7 odorous volatile organic compounds in the ambient air surrounding a large industrial complex. Atmospheric Environment 44: 3492–502. doi:10.1016/j.atmosenv.2010.06.021
   Web of Science ®Google Scholar
• Kalabokas, P. D., J. Hatzianestis, J. G. Bartzis, and P. Papagiannakopoulos. 2001. Atmospheric concentrations of saturated and aromatic hydrocarbons around a Greek oil refinery. Atmospheric Environment 35: 2545–55. doi:10.1016/S1352-2310(00)00423-4
   Web of Science ®Google Scholar
• Karppinen, A., J. Kukkonen, T. Elolähde, M. Konttinen, T. Koskentalo, and E. Rantakrans. 2000. A modelling system for predicting urban air pollution: Model description and applications in the Helsinki metropolitan area. Atmospheric Environment 34(22): 3723–33. doi:10.1016/S1352-2310(00)00074-1
   Web of Science ®Google Scholar
• Leuchner, M., and B. Rappengluck. 2010. VOC source–receptor relationship in Houston during TexAQS-II. Atmospheric Environment 44(33): 4056–67. doi:10.1016/j.atmosenv.2009.02.029
   Web of Science ®Google Scholar
• Levene, H. 1960. Robust test for equality of variances. In Contributions to probability and statistics: Essays in honor of Harold Hotelling, ed. I. Olkin, S. G. Ghurye, W. Hoeffding, W. G. Madow, H. B. Mann, 278–92. Stanford, CT: Stanford University Press.
   Google Scholar
• Li, Y., J. Wang, Z. Chen, and X. Zhou. 2001. Artificial neural network for the quantitative analysis of air toxic VOCs. Analytical Letters 34(12): 2203–19. doi:10.1081/AL-100106851
   Web of Science ®Google Scholar
• Lin, Y., and W. G. Cobourn. 2007. Fuzzy system models combined with nonlinear regression for daily ground-level ozone predictions. Atmospheric Environment 41: 3502–13. doi:10.1016/j.atmosenv.2006.11.060
   Web of Science ®Google Scholar
• MacIntosh, D. L., J. H. Stewart, T. A. Myatt, J. E. Sabato, G. C. Flowers, K. W. Brown, D. J. Hlinka, and D. A. Sullivan. 2010. Use of CALPUFF for exposure assessment in a near-field, complex terrain setting. Atmospheric Environment 44(2): 262–70. doi:10.1016/j.atmosenv.2009.09.023
   Web of Science ®Google Scholar
• Matthias, V. 2008. The aerosol distribution in Europe derived with the Community Multiscale Air Quality (CMAQ) model: Comparison to near surface in situ and sunphotometer measurements. Atmospheric Chemistry and Physics Discussions 8: 1457–503. doi:10.5194/acpd-8-1457-2008
   Google Scholar
• Nebot, A., V. Mugica, and A. Escobet. 2008. Ozone prediction based on meteorological variables: A fuzzy inductive reasoning approach. Atmospheric Chemistry and Physics Discussions 8: 12343–70. doi:10.5194/acpd-8-12343-2008
   Google Scholar
• Olesen, H. R., R. Berkowicz, and P. Lofstrom. 2007. OML: Review of model formulation. Technical Report 609. National Environmental Research Institute, Denmark.
   Google Scholar
• Owen, B., H. A. Edmunds, D. J. Carruthers, and R. J. Singles. 2000. Prediction of total oxides of nitrogen and nitrogen dioxide concentrations in a large urban area using a new generation urban scale dispersion model with integral chemistry model. Atmospheric Environment 34(3): 397–406. doi:10.1016/S1352-2310(99)00332-5
   Web of Science ®Google Scholar
• Paatero, P. 2000. User’s guide for positive matrix factorization programs PMF2 and matter across Spain in the CALIOPE modeling system. Atmospheric Environment 46: 376–96.
   Google Scholar
• Pandya, G. H., A. G. Gavane, A. D. Bhanarkar, and V. K. Kondawar. 2006. Concentrations of volatile organic compounds (VOCs) at an oil refinery. The International Journal of Environmental Studies 63(3): 337–51. doi:10.1080/00207230500241918
   Google Scholar
• Pay, M. T., M. Piot, O. Jorba, S. Gassó, M. Gonçalves, S. Basart, D. Dabdub, P. Jiménez-Guerrero, and J. M. Baldasano. 2010. A full year evaluation of the CALIOPE-EU air quality modeling system over Europe for 2004. Atmospheric Environment 44(27): 3322–42. doi:10.1016/j.atmosenv.2010.05.040
   Web of Science ®Google Scholar
• Petelin, D., A. Grancharova, and J. Kocijan. 2013. Evolving Gaussian process models for prediction of ozone concentration in the air. Simulation Modelling Practice and Theory 33: 68–80. doi:10.1016/j.simpat.2012.04.005
   Web of Science ®Google Scholar
• Rappaport, S. M., and L. L. Kupper. 2004. Variability of environmental exposures to volatile organic compounds. Journal of Exposure Analysis and Environmental Epidemiology 14(1): 92–107. doi:10.1038/sj.jea.7500309
   PubMedGoogle Scholar
• Rexeis, M., and S. Hausberger. 2009. Trend of vehicle emission levels until 2020—Prognosis based on current vehicle measurements and future emission legislation. Atmospheric Environment 43: 4689–98. doi:10.1016/j.atmosenv.2008.09.034
   Web of Science ®Google Scholar
• Romanian Parliament. 2011. http://mmediu.ro/file/2011-12-29_legislatie_calitate_aer_legea104din2011calitate%20aer.pdf (accessed July 27, 2014) (in Romanian).
   Google Scholar
• Shewchuk, J. R. 1994. http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf (accessed July 27, 2014).
   Google Scholar
• Sprecht, D. F. 1991. A general regression neural network. IEEE Transactions on Neural Networks 2(6): 568–76. doi:10.1109/72.97934
   PubMed Web of Science ®Google Scholar
• Terasvirta, T., D. van Dijk, and M. C. Medeiros. 2005. Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination. International Journal of Forecasting 21(4): 755–74. doi:10.1016/j.ijforecast.2005.04.010
   Web of Science ®Google Scholar
• U. S. Environmental Protection Agency. 1999. EPA/625/R-96/010b, Compendium for the determination of toxic organic compounds in ambient air, 2nd edition, Center for Environmental Research Information Office of research and Development, U.S.
   Google Scholar
• Wei, W., S. Cheng, G. Li, G. Wang, and H. Wang. 2014. Characteristics of volatile organic compounds (VOCs) emitted from a petroleum refinery in Beijing, China. Atmospheric Environment 89: 358–66. doi:10.1016/j.atmosenv.2014.01.038
   Web of Science ®Google Scholar
• World Health Organization. 2005. http://www.who.int/phe/health_topics/outdoorair/outdoorair_aqg/en/ (accessed July 27, 2014).
   Google Scholar
• Zhang, G. P. 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50: 159–75. doi:10.1016/S0925-2312(01)00702-0
   Web of Science ®Google Scholar
• Zou, B., F. B. Zhan, J. G. Wilson, and Y. Zeng. 2010. Performance of AERMOD at different time scales. Simulation Modelling Practice anland Theory 18(5): 612–23. doi:10.1016/j.simpat.2010.01.005
   Web of Science ®Google Scholar