Open Access
Issue
E3S Web Conf.
Volume 32, 2018
EENVIRO 2017 Workshop - Advances in Heat and Transfer in Built Environment
Article Number 01010
Number of page(s) 7
DOI https://doi.org/10.1051/e3sconf/20183201010
Published online 21 February 2018
  1. Gulia, S., et al., Urban air quality management-A review. Atmospheric Pollution Research, 2015. 6(2): p. 286-304. [CrossRef] [Google Scholar]
  2. OECD, The Economic Consequences of Outdoor Air Pollution. OECD Publishing. [Google Scholar]
  3. WHO, Ambient air pollution: A global assessment of exposure and burden of disease, W.H. Organization, Editor. 2016. [Google Scholar]
  4. WHO, Reducing global healh risks through mitigation of short-lived climate pollutants. Scoping report for policymakers, W.H. Organization, Editor. 2015. [Google Scholar]
  5. WHO, Global surveillance, prevention and control of chronic respiratory diseases. A comprehensive approach, ed. W.H. Organization. 2007. [Google Scholar]
  6. Diapouli, E., et al., Evolution of air pollution source contributions over one decade, derived by PM10 and PM2.5 source apportionment in two metropolitan urban areas in Greece. Atmospheric Environment, 2017. 164(Supplement C): p. 416-430. [CrossRef] [Google Scholar]
  7. Daher, N., et al., Characterization, sources and redox activity of fine and coarse particulate matter in Milan, Italy. Atmospheric Environment, 2012. 49(Supplement C): p. 130-141. [CrossRef] [Google Scholar]
  8. Agency, E.E., Air quality in Europe — 2017 report. 2017. [Google Scholar]
  9. Kim, K.-H., E. Kabir, and S. Kabir, A review on the human health impact of airborne particulate matter. Environment international, 2015. 74: p. 136-143. [CrossRef] [PubMed] [Google Scholar]
  10. EPA, Clean Air Act Overview-Air Pollution: Current and Future Challenges, U.S.E.P. Agency, Editor. 1990. [Google Scholar]
  11. ORGANIZATION, W.H., Indoor air pollutants:exposure and health effects, in Reports and Studies, EURO, Editor. 1982, WHO: Copenhagen. [Google Scholar]
  12. ORGANIZATION, W.H., Indoor air quality research, in Reports and Studies, EURO, Editor. 1984, WHO: Copenhagen. [Google Scholar]
  13. Redlich, C.A., J. Sparer, and M.R. Cullen, Sick-building syndrome. The Lancet, 1997. 349(9057): p. 1013-6. [CrossRef] [Google Scholar]
  14. Zhong, J., X.-M. Cai, and W.J. Bloss, Coupling dynamics and chemistry in the air pollution modelling of street canyons: A review. Environmental Pollution, 2016. 214: p. 690-704. [CrossRef] [Google Scholar]
  15. Peng, H., Air quality prediction by machine learning methods. 2015. [Google Scholar]
  16. Adams, M.D. and P.S. Kanaroglou, Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models. Journal of Environmental Management, 2016. 168: p. 133-141. [CrossRef] [PubMed] [Google Scholar]
  17. Hoek, G., et al., Estimation of long-term average exposure to outdoor air pollution for a cohort study on mortality. J Expo Anal Environ Epidemiol, 2001. 11(6): p. 459-69. [CrossRef] [PubMed] [Google Scholar]
  18. Vardoulakis, S., et al., Modelling air quality in street canyons: a review. Atmospheric environment, 2003. 37(2): p. 155-182. [CrossRef] [Google Scholar]
  19. Font, A., et al., Degradation in urban air quality from construction activity and increased traffic arising from a road widening scheme. Science of The Total Environment, 2014. 497-498: p. 123-132. [CrossRef] [Google Scholar]
  20. Atkinson, R., Atmospheric chemistry of VOCs and NO x. Atmospheric environment, 2000. 34(12): p. 2063-2101. [CrossRef] [Google Scholar]
  21. Svensson, U., PHOENICS in environmental flows. A review of applications at SMHI. Lecture Notes in Engineering, 1986. 18: p. 87-96. [CrossRef] [Google Scholar]
  22. Baklanov, A., Application of CFD Methods for Modelling in Air Pollution Problems: Possibilities and Gaps. Environmental Monitoring and Assessment, 2000. 65(1): p. 181-189. [CrossRef] [Google Scholar]
  23. Chu, A.K.M., R.C.W. Kwok, and K.N. Yu, Study of pollution dispersion in urban areas using Computational Fluid Dynamics (CFD) and Geographic Information System (GIS). Environmental Modelling & Software, 2005. 20(3): p. 273-277. [CrossRef] [Google Scholar]
  24. Neofytou, P., et al., Computational Fluid Dynamics Modelling of the Pollution Dispersion and Comparison with Measurements in a Street Canyon in Helsinki. Environmental Modeling & Assessment, 2008. 13(3): p. 439-448. [CrossRef] [Google Scholar]
  25. Haghighat, F. and P.A. Mirzaei, Impact of non-uniform urban surface temperature on pollution dispersion in urban areas. Building Simulation, 2011. 4(3): p. 227. [CrossRef] [Google Scholar]
  26. Jeanjean, A.P.R., et al., A CFD study on the effectiveness of trees to disperse road traffic emissions at a city scale. Atmospheric Environment, 2015. 120: p. 1-14. [CrossRef] [Google Scholar]
  27. McNabola, A., B.M. Broderick, and L.W. Gill, A numerical investigation of the impact of low boundary walls on pedestrian exposure to air pollutants in urban street canyons. Science of The Total Environment, 2009. 407(2): p. 760-769. [CrossRef] [Google Scholar]
  28. Toja-Silva, F., et al., CFD simulation of CO2 dispersion from urban thermal power plant: Analysis of turbulent Schmidt number and comparison with Gaussian plume model and measurements. Journal of Wind Engineering and Industrial Aerodynamics, 2017. 169: p. 177-193. [CrossRef] [Google Scholar]
  29. Lal, B. and S.S. Tripathy, Prediction of dust concentration in open cast coal mine using artificial neural network. Atmospheric Pollution Research, 2012. 3(2): p. 211-218. [CrossRef] [Google Scholar]
  30. Nejadkoorki, F. and S. Baroutian, Forecasting Extreme PM10 Concentrations Using Artificial Neural Networks. International Journal of Environmental Research, 2012. 6(1): p. 277-284. [Google Scholar]
  31. Luecken, D.J., W.T. Hutzell, and G.L. Gipson, Development and analysis of air quality modeling simulations for hazardous air pollutants. Atmospheric Environment, 2006. 40(26): p. 5087-5096. [CrossRef] [Google Scholar]
  32. Kurt, A., et al., An online air pollution forecasting system using neural networks. Environment International, 2008. 34(5): p. 592-598. [CrossRef] [PubMed] [Google Scholar]
  33. Wilson, R., Air Pollution, the Automobile, and Public Health. Environment: Science and Policy for Sustainable Development, 1989. 31(4): p. 25-27. [CrossRef] [Google Scholar]
  34. Solazzo, E., et al., Model evaluation and ensemble modelling of surface-level ozone in Europe and North America in the context of AQMEII. Atmospheric Environment, 2012. 53: p. 60-74. [CrossRef] [Google Scholar]
  35. Cartier, Y., T. Benmarhnia, and A. Brousselle, Tool for assessing health and equity impacts of interventions modifying air quality in urban environments. Evaluation and Program Planning, 2015. 53: p. 1-9. [CrossRef] [PubMed] [Google Scholar]
  36. Prasad, K., A.K. Gorai, and P. Goyal, Development of ANFIS models for air quality forecasting and input optimization for reducing the computational cost and time. Atmospheric Environment, 2016. 128: p. 246-262. [CrossRef] [Google Scholar]
  37. Borrego, C., et al., Urban scale air quality modelling using detailed traffic emissions estimates. Atmospheric Environment, 2016. 131: p. 341-351. [CrossRef] [Google Scholar]
  38. Bowman, K.W., Toward the next generation of air quality monitoring: Ozone. Atmospheric Environment, 2013. 80: p. 571-583. [CrossRef] [Google Scholar]
  39. Martin, R.V., Satellite remote sensing of surface air quality. Atmospheric Environment, 2008. 42(34): p. 7823-7843. [CrossRef] [Google Scholar]
  40. Hu, D., et al., Urban air quality, meteorology and traffic linkages: Evidence from a sixteen-day particulate matter pollution event in December 2015, Beijing. Journal of environmental sciences (China), 2017. 59: p. 30-38. [CrossRef] [PubMed] [Google Scholar]
  41. Rashid, B. and M.H. Rehmani, Applications of wireless sensor networks for urban areas: A survey. Journal of Network and Computer Applications, 2016. 60: p. 192-219. [CrossRef] [Google Scholar]
  42. Al-Ali, A.R., I. Zualkernan, and F. Aloul, A Mobile GPRS-Sensors Array for Air Pollution Monitoring. IEEE Sensors Journal, 2010. 10(10): p. 1666-1671. [CrossRef] [Google Scholar]
  43. Hubbell, B.J., et al., Understanding social and behavioral drivers and impacts of air quality sensor use. Science of The Total Environment, 2018. 621: p. 886-894. [CrossRef] [Google Scholar]
  44. Hasenfratz, D., et al., Participatory air pollution monitoring using smartphones. Mobile Sensing, 2012. 1: p. 1-5. [Google Scholar]
  45. Sammarco, M., et al., Using geosocial search for urban air pollution monitoring. Pervasive and Mobile Computing, 2017. 35: p. 15-31. [CrossRef] [Google Scholar]
  46. Ahmed, A.A.N., et al., A Participatory Sensing Framework for Environment Pollution Monitoring and Management. arXiv preprint arXiv:1701.06429, 2017. [Google Scholar]
  47. Sá, E., et al., Climate change and pollutant emissions impacts on air quality in 2050 over Portugal. Atmospheric Environment, 2016. 131: p. 209-224. [CrossRef] [Google Scholar]
  48. Santosa, S.J., T. Okuda, and S. Tanaka, Air Pollution and Urban Air Quality Management in Indonesia. CLEAN – Soil, Air, Water, 2008. 36(5-6): p. 466-475. [CrossRef] [Google Scholar]
  49. Pulles, M., Impact of selected policy measures on Europe's air quality. 2010. [Google Scholar]

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