Abstract
Karachi (24° 37.38¢ N, 66° 54.42¢ E) is one of the mega cities of Pakistan. In general, deteriorating urban air quality in developing countries is a worsening environmental problem and causing damage to human health. The urban atmospheric pollution is several times higher than the limits set by the WHO. Being industrial city, the rate of increase of traffic volume has been exponential during the last decade in Karachi. More or less, 90% atmospheric pollution is related to vehicle emissions. To gauge and forecast future traffic volume and urban pollution level mathematical models are needed. This communication attempts to model the urban traffic population evolution and the atmospheric pollution levels during the past 25 years. The traffic model shows that total traffic volume in Karachi was 1 million in 1999. In 2008 it reached 2 million and in 2012 the traffic volume crossed 3 million verifying published data. According to this forecast model, it is also important to note that the total traffic volume in Karachi will go to 4 million, 5 million, and 6 million in the years 2015, 2018, and 2020 respectively. Auto Regressive Integrated Moving Average, ARIMA (2, 1, 2) model is found to be adequate model to capture Karachi urban atmospheric pollution variation. The model is the first of its kind for the region considered. As a further application, we develop an empirical model of local atmospheric pollution fluctuations as determined by urban traffic volume. The work should provide a basis for other applications, including urban planning,urban-regional air quality management, design of efficient energy programs, etc.
References
Hussain MA, et al. Persistency Analysis of Cyclone History in Arabian Sea. The Nucleus 2011; 48(4): 2011.
Berezansky L, Braverman E, Idels L. Nicholson's blowflies differential equations revisited: main results and open problems. Appl Math Model 2010; 34: 1405-17. http://dx.doi.org/10.1016/j.apm.2009.08.027
Zhou H, Zhou ZF, Wang Q. Positive almost periodic solution for a class of Lasota Wazewska model with infinite delays. Appl Math Comp 2011; 218: 4501-506. http://dx.doi.org/10.1016/j.amc.2011.10.031
Hussain MA, et al. Arabian Seawater Temperature Fluctuations in the Twentieth Century. J Basic Appl Sci 2012; 8(1): 105-109. http://dx.doi.org/10.6000/1927-5129.2012.08.01.24
Hussain MA, Iqbal MJ, Soomro S. Urban Wind Speed Analysis in Global Climate Change Perspective: Karachi as a Case Study. Int J Geo Sci 2012; 3: 1000-1009. http://dx.doi.org/10.4236/ijg.2012.325100
Hussain MA, et al. Forecast Models for Urban Extreme Temperatures: Karachi Region as a Case Study. The Nucleus 2010; 47(4): 301-11.
Hussain MA. Mathematical Aspects of The Impact of Urban Greenhouse Gas Emissions on Global Warming, PhD Thesis. Federal Urdu University of Arts, Science and Technology, Karachi, Pakistan 2006.
Ghauri B, et al. Enrichment of Toxic Lead in Karachi Aerosols. Quarterly Sci Vision 1999; 5(2): 10.
Beg MAA, et al. Air pollution in Karachi. Pak J Sci Ind Res 1987; 30(1).
Khan A. Interrelationships between demographic Factors, Development and the Environment in the ESCAP Region. Asia- Pacific Population J 1994; 9(3): 37.
Yousufzai AHK. Heavy Metals Accumulation in Road Side Vegetation of Urban Areas of Karachi. Pak J Sci Ind Res 2001; 44(1).
Yousufzai AHK. Lead and the heavy metals in the street dust of metropolitan city of Karachi. Pak J Sci Ind Res 1991; 40(1).
Hasan A. Understanding Karachi, City Press, Karachi 1999.
Qadri WA, et al. Survey and Study of Air pollution in Karachi, Civil Engg, Project NED University of Engineering and Technology, Karachi 1988.
Sajjad, et al. The preliminary study of urbanization, fossil fuels consumptions andCO2 emmission in Karachi. Afr J Biotecnol 2010; 9(13): 1941-48.
Qureshi S. The fast growing megacity Karachi as a frontier of environmental challenges: Urbanization and contemporary urbanism issues. J Geogr Regional Planning 2010; 3(11): 306-21.
http://beta.pique.pk/environment/04-Sep-2012/going-up-in-smog (Accessed 20-11-2012).
Box JEP, Jenkins G. Time series analysis, forecasting and control (Rev. Ed.), Boca Raton, Fla: Holden-Day 1976.
Granger C, Teräsvirta T. Modelling nonlinear economic relationships. Oxford University Press, Oxford 1990.
Tong H. Non-linear time series: A dynamical systems approach. Oxford statistical science series, Oxford University Press, Oxford 1990; Vol. 6.
Zhang G, Patuwo B, Hu M. Forecasting with artificial neural networks: The state of the art. Int J Forecasting 1998; 14: 35. http://dx.doi.org/10.1016/S0169-2070(97)00044-7
Cooray TMJA. Applied Time Series Analysis. Narosa Publishing House, New Delhi 2008.
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Copyright (c) 2013 M. Arif Hussain, Syed Ghayasuddin, Shaheen Abbas , M. Rashid Kamal Ansari