Open Access
E3S Web Conf.
Volume 101, 2019
2019 10th International Conference on Environmental Science and Development (ICESD 2019)
Article Number 03001
Number of page(s) 6
Section Air and Climate
Published online 14 June 2019
  1. W. H. Organization, Monitoring ambient air quality for health impact assessment [Google Scholar]
  2. U. Gehring, A. H. Wijga, M. Brauer, P. Fischer, J. C. de Jongste, M. Kerkhof, M. Oldenwening, H. A. Smit, B. Brunekreef, Traffic-related Air Pollution and the Development of Asthma and Allergies during the first 8 Years of Life, American Journal of Respiratory and Critical Care Medicine 181 (6) (2010) 596-603. doi:10.1164/rccm.200906-0858OC. [CrossRef] [PubMed] [Google Scholar]
  3. Act on the Prevention of Harmful Effects on the Environment Caused by Air Pollution, Noise, Vibration and Similar Phenomena. [Google Scholar]
  4. M. M. Rahman, B. Yeganeh, S. Clifford, L. D. Knibbs, L. Morawska, Development of a land use regression model for daily NO2 and NOx concentrations in the Brisbane metropolitan area, Australia, Environmental Modelling & Software 95 (2017) 168-179. doi:10.1016/j.envsoft.2017.200 06.029. [CrossRef] [Google Scholar]
  5. K. Bashir Shaban, A. Kadri, E. Rezk, Urban Air Pollution Monitoring System With Forecasting Models, IEEE Sensors Journal 16 (8) (2016) 2598-2606. doi:10.1109/JSEN.2016.2514378. [CrossRef] [Google Scholar]
  6. B. Elen, J. Peters, M. Poppel, N. Bleux, J. Theunis, M. Reggente, A. Standaert, The Aeroflex: A Bicycle for Mobile Air Quality Measurements, Sensors 13 (12) (2012) 221-240. doi:10.3390/s130100221. [Google Scholar]
  7. W. Liu, X. Li, Z. Chen, G. Zeng, T. Leon, J. Liang, G. Huang, Z. Gao, 210 S. Jiao, X. He, M. Lai, Land use regression models coupled with meteorology to model spatial and temporal variability of NO2 and PM10 in Changsha, China, Atmospheric Environment 116 (2015) 272-280. doi:10.1016/j.atmosenv.2015.06.056. [CrossRef] [Google Scholar]
  8. J. Van den Bossche, J. Peters, J. Verwaeren, D. Botteldooren, J. Theunis, B. De Baets, Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset, Atmospheric Environment 105 (2015) 148-161. doi:195 10.1016/j.atmosenv.2015.01.017. [CrossRef] [Google Scholar]
  9. Z. Ross, P. B. English, R. Scalf, R. Gunier, S. Smorodinsky, S. Wall, M. Jerrett, Nitrogen dioxide prediction in Southern California using land use regression modeling: potential for environmental health analyses, Journal of Exposure Science and Environmental Epidemiology 16 (2006) 106-114.doi:10.1038/sj.jea.7500442. [CrossRef] [Google Scholar]
  10. S.I.V. Sousa, F.G. Martins, M.C.M. Alvim-Ferraz, M.C. Pereira, 2007. Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environmental Modelling & Software 22, 97-103. doi: 10.1016/j.envsoft.2005.12.002 [CrossRef] [Google Scholar]
  11. Y. Rybarczyk, R. Zalakeviciute. Machine learning approach to forecasting urban pollution. IEEE Ecuador Technical Chapters Meeting (ETCM), 2016, 1-6. doi: 10.1109/ETCM.2016.7750810 [Google Scholar]
  12. C. Yan, S. Xu, Y. Huang, Y. Huang, Z. Zhang. Two-phase Neural Network Model for Pollution Concentrations Forecasting. Fifth International Conference on Advanced Cloud and Big Data, 2017. doi: 10.1109/CBD.2017.73 [Google Scholar]
  13. M. M. Dedovic, I. Turkovic, T. Konjic, S. Avdakovic, N. Dautbasic. Forecasting PM10 concentrations using neural networks and system for improving air quality. XI International Symposium on Telecommunications (BIHTEL), October 24-26, 2016. doi: 10.1109/BIHTEL.2016.7775721 [Google Scholar]
  14. B. Owen, H. A. Edmunds, D. J. Carruthers, R. J. Singles, 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) (2000) 397-406. [CrossRef] [Google Scholar]
  15. S. Tunlathorntham, S. Thepanondh, Prediction of Ambient Nitrogen Dioxide Concentrations in the Vicinity of Industrial Complex Area, Thailand, Air, Soil and Water Research 10 (2017) 1178622117700906. [Google Scholar]
  16. U. G. I. (GPO), J. Orme-Zavaleta, Office of Research and Development; Ambient Air Monitoring Reference and Equivalent Methods: Designation of One New Equivalent Method, Federal Register 82 (90). [Google Scholar]
  17. DIN EN 14211 Ambient air Standard method for the measurement of the concentration of nitrogen dioxide and nitrogen monoxide by chemiluminescence, Deutsches Institut für Normung eV, Berlin. [Google Scholar]
  18. S. Gilde, GAW Brief des DWD – Der CAPS-Monitor, ein neues Instrument zur Messung von Stickstoffdioxid in Umgebungslust, [Google Scholar]
  19. R. Menard, M. Deshaies-Jacques, Evaluation of Analysis by Cross-Validation. Part I: Using Verification Metrics, Atmosphere 2018 (9) (2018) 86. doi:10.3390/atmos9030086. [Google Scholar]
  20. P. Refaeilzadeh, L. Tang, H. Liu, Cross-validation (2009) 532-538. [Google Scholar]
  21. T.-T. Wong, Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation, Pattern Recognition 48 (9) (2015) 2839-2846. doi:10.1016/j.patcog.2015.03.009. [CrossRef] [Google Scholar]
  22. Alho, J. and B. Spencer. (2005). Statistical Demography and Forecasting. Dordrecht, The Netherlands: Springer . W. Alonso and P. Starr (Eds.). The Politics of Numbers. New York: Russell Sage. [Google Scholar]
  23. M. V. Shcherbakov, A. Brebels, N. L. Shcherbakova, A. P. Tyukov, T. A. Janovsky, V. A. Kamaev, A survey of forecast error measures, World Applied Sciences Journal 24 (2013) 171-176. doi: 10.5829/idosi.wasj.2013.24.itmies.80032. [Google Scholar]

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