Advertisement

An Overview of Theoretical Dynamics of Air Pollution

  • Moses Eterigho EmetereEmail author
Chapter
Part of the Studies in Big Data book series (SBD, volume 54)

Abstract

In this chapter the scope is on the outdoor air pollution. Outdoor air pollution can be natural or man-made/artificial. Most natural air pollutions are connected to gas emission from volcanic eruption, dust storm (Sahara Desert in the West Africa region), particulate matter (PM) carried by wind, industries. Therefore, modelling outdoor air pollution is somewhat difficult because it is not a closed system. A closed system is a system where the component parameters can be controlled or determined at each step of the experiment. The outdoor pollution is an open system. In this chapter, the different scenarios were considered. The different mathematical models on out door pollution was discussed. Lastly, the different type of ground measuring instruments and their specific functions were highlighted.

References

  1. ACTRAC. (1998). Environmental control—Unit 2 air pollution, Cert. In Chem. Plant Skills Resource.Google Scholar
  2. Bahnemann, D. W., & Robertson, P. K. J. (2015). Environmental photochemistry part III. In The handbook of environmental chemistry (p. 307). New York: Springer. ISBN-13: 978-3662467947, ISBN-10: 3662467941.Google Scholar
  3. Bock, O., Bouin, M. -N., Doerflinger, E., Collard, P., Masson, F., Meynadier, R., et al. (2008). The West African monsoon observed with ground based GPS receivers during AMMA. Journal of Geophysics Research, 113, D21105.Google Scholar
  4. Boubel, et al. (1994). Fundamentals of air pollution (3rd ed). Academic Press.Google Scholar
  5. Crooks, J., & Ozkaynak, H. (2014). Simultaneous statistical bias correction of multiple PM2.5 species from a regional photochemical grid model. Atmospheric Environment, 95, 126–141.CrossRefGoogle Scholar
  6. EEA. (2016). Sources of air pollution. https://www.eea.europa.eu/publications/2599XXX/page010.html#note. Accessed August 25, 2018.
  7. Emetere, M. E. (2016a). Statistical examination of the aerosols loading over Mubi-Nigeria: The satellite observation analysis. Geographica Panonica, 20(1), 42–50.Google Scholar
  8. Emetere, M. E. (2016b). Numerical modelling of West Africa regional scale aerosol dispersion. Thesis submitted to Covenant University.Google Scholar
  9. Emetere, M. E., & Akinyemi, M. L. (2013). Modeling of generic air pollution dispersion analysis from cement factory. Analele Universitatii din Oradea-Seria Geografie, 231123-628, 181–189.Google Scholar
  10. Emetere, M. E., & Akinyemi, M. L. (2017). Documentation of atmospheric constants over Niamey, Niger: A theoretical aid for measuring instruments. Meteorological Applications, 24(2), 260–267.CrossRefGoogle Scholar
  11. Emetere, M. E., Akinyemi, M. L., & Akinojo, O. (2015a). A novel technique for estimating aerosol optical thickness trends using meteorological parameters. 2015 PIAMSEE: AIP Conference Proceedings, 1705(1), 020037.Google Scholar
  12. Emetere, M. E., Akinyemi, M. L., & Uno, U. E. (2015b). Computational analysis of aerosol dispersion trends from cement factory. In IEEE Proceedings 2015 International Conference on Space Science & Communication (pp. 288–291).Google Scholar
  13. Emetere, M. E., Sanni, S. E., Emetere, J. M., & Uno, U. E. (2017a). Thermal infrared remote sensing of hydrocarbon in Lagos-Southern Nigeria: Application of the thermographic model. International Geomate Journal, 13(39), 33–45.Google Scholar
  14. Emetere, M. E., Esisio, F., & Oladapo, F. (2017b). Satellite observation analysis of aerosols loading effect over Monrovia-Liberia. Journal of Physics: Conference Series, 852(1), art. no. 012009.  https://doi.org/10.1088/1742-6596/852/1/012009.
  15. Emetere, M. E., Sanni, S. E., & Tunji-Olayeni, P. (2017c). Atmospheric configurations of aerosols loading and retention over Bolgatanga-Ghana. Journal of Physics: Conference Series, 852(1), art. no. 012007.  https://doi.org/10.1088/1742-6596/852/1/012007.
  16. Gauderman, W. J., Avol, E., Gilliland, F., Vora, H., Thomas, D., Berhane, K. R., et al. (2004). The Effect of Air Pollution on Lung Development from 10 to 18 Years of Age, J Med, 351, 1057.Google Scholar
  17. Gazala, H., Venkataraman, C., Isabelle, C., Ramachandran, S., Olivier, B., & Shekar, M. R. (2006). Seasonal and interannual variability in absorbing aerosols over India derived from TOMS: Relationship to regional meteorology and emissions. Atmospheric Environment, 40, 1909–1921.CrossRefGoogle Scholar
  18. GSL. (2018). Getting started in atmospheric dispersion modelling—An introduction. https://guides.co/g/atmospheric-dispersion-modelling-an-introduction/24917. Accessed January 7, 2018.
  19. Gualtieri, G., & Tartaglia, M. (1998). Predicting urban traffic air pollution: A GIS framework. Transportation Research, D3(5), 329–336.Google Scholar
  20. Hanna, S. R., Drivas, P. J., & Chang, J. C. (1996). Guidelines for Use of Vapor Cloud Dispersion Models. AIChE/CCPS, 345 East 47th St., New York, NY 10017, 285 pp.Google Scholar
  21. Holmes, N. S., & Morawska, L. (2006). A review of dispersion modelling and its application to the dispersion of particles: an overview of different dispersion models available. Atmospheric environment, 40, 5902–5928.Google Scholar
  22. Jacobson, M. Z., Kaufman, Y. J., & Rudich, Y. (2007). Examining feedbacks of aerosols to urban climate with a model that treats 3-D clouds with aerosol inclusions. Journal of Geophysical Research, 112, D24205.  https://doi.org/10.1029/2007JD008922.CrossRefGoogle Scholar
  23. Jerret, M., Arain, A., Pavlos, K., Bernardo, B., Dimitri, P., Talar, S., et al. (2005). A review and evaluation of intraurban air pollution exposure model. Journal of Exposure Analysis and Environmental Epidemiology, 15, 185–204.CrossRefGoogle Scholar
  24. Khaled, S. M. E., Soad, M. E., & Maha, S. E. (2014). Modelling of atmospheric dispersion with dry deposition: An application on a research reactor. Revista Brasileira de Meteorologia, 29(3), 331–337.CrossRefGoogle Scholar
  25. Lindén, J., Thorsson, S., Boman, R., & Holmer, B. (2012). Urban climate and air pollution in Ouagadougou, Burkina Faso: An overview of results from five field studies (pp. 1–88). University of Gothenburg. http://hdl.handle.net/2077/34289.
  26. Macdonald, R. (2003). Theory and objectives of air dispersion modelling. Modelling Air Emissions for Compliance, Wind Engineering, MME, 474A, 1–27.Google Scholar
  27. McKibbin, R. (2008). Mathematical modeling of aerosol transport and deposition: Analytic formulae for fast computation. In Proceedings of International Congress on Environmental Modeling (pp 1420–1430).Google Scholar
  28. Norris, S. J., Ian, M. B., & Dominic, J. S. (2013). A wave roughness Reynolds number parameterization of the sea spray source flux. Geophysical Research Letters, 40, 4415–4419.CrossRefGoogle Scholar
  29. Nuret, M., Lafore, J. P., Bock, O., Guichard, F., Agust́ ı-Panareda, A., Ngamini, J. B., et al. (2008). Correction of humidity bias for Vaısala RS80 sondes during AMMA 2006 observing period. Journal of Atmospheric Oceanic Technology, 25, 2152–2158.Google Scholar
  30. O’Neill, M. S., Jerrett, M., Kawachi, I., Levy, J. I., Cohen, A. J., Gouveia, N., et al. (2003). Health, wealth, and air pollution: Advancing theory and methods. Environmental Health Perspectives, 111, 1861–1870.Google Scholar
  31. Standards Australia. (1987). AS2922—A guide for the siting of sampling units.Google Scholar
  32. Sutton, O. G. (1932). A theory of eddy diffusion in the atmosphere. Proceedings of the Royal Society London A, 135,143–165.Google Scholar
  33. Tirabassi, T., Moreira, D. M., Vilhena, M. T., & da Costa, C. P. (2010). Comparison between non-gaussian puff model and a model based on a time-dependent solution of advection-diffusion equation. Journal of Environmental Protection, 1, 172–178.CrossRefGoogle Scholar
  34. Vladutescu, D. V., Bomidi, L. M., Barry, M. G., Qi, Z., & Shan, Z. (2013). Aerosol transport and source attribution using sun photometers, models and in-situ chemical composition measurements. IEEE Transactions on Geoscience and Remote Sensing, 51(7), 3803–3811.CrossRefGoogle Scholar
  35. Walcek, C. J. (2004). A Gaussian dispersion/plume model explicitly accounting for wind shear. https://ams.confex.com/ams/pdfpapers/79742.pdf. Accessed January 9, 2018.
  36. Wilson, R., Luce, H., Hashiguchi, H., Nishi, N., & Yabuki, Y. (2014). Energetics of persistent turbulent layers underneath mid-level clouds estimated from concurrent radar and radiosonde data. Journal of Atmospheric and Solar-Terrestrial Physics, 118(A), 78–89.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of PhysicsCovenant UniversityOtaNigeria
  2. 2.Department of Mechanical Engineering ScienceUniversity of JohannesburgJohannesburgSouth Africa

Personalised recommendations