Research Article A Study of Probability Models in Monitoring Environmental Pollution in Nigeria P. E. Oguntunde, 1 O. A. Odetunmibi, 1 and A. O. Adejumo 2 1 Department of Mathematics, Covenant University, Ota, Ogun State, Nigeria 2 Department of Statistics, University of Ilorin, Ilorin, Nigeria Correspondence should be addressed to A. O. Adejumo; aodejumo@gmail.com Received 29 January 2014; Revised 17 April 2014; Accepted 18 April 2014; Published 5 May 2014 Academic Editor: Zhidong Bai Copyright © 2014 P. E. Oguntunde et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In Lagos State, Nigeria, pollutant emissions were monitored across the state to detect any significant change which may cause harm to human health and the environment at large. In this research, three theoretical distributions, Weibull, lognormal, and gamma distributions, were examined on the carbon monoxide observations to determine the best fit. e characteristics of the pollutant observation were established and the probabilities of exceeding the Lagos State Environmental Protection Agency (LASEPA) and the Federal Environmental Protection Agency (FEPA) acceptable limits have been successfully predicted. Increase in the use of vehicles and increase in the establishment of industries have been found not to contribute significantly to the high level of carbon monoxide concentration in Lagos State for the period studied. 1. Introduction It is common knowledge that population growth and glob- alization have become the major drivers of pollution. Out of the various forms of pollution, a large number of studies that investigated the relationship between air quality and health effects cited air pollution as the major environmental issue of concern to the community. Increase in hospitalization, emer- gency room attendance, and decreased lung function have been associated with the following common air pollutants: carbon monoxide (CO), nitrogen oxides (NO  ), inhalable particles (measured as PM 10 ), photochemical oxidants (mea- sured as ozone), and sulphur dioxide SO 2 . Air pollution is defined as the presence in the outdoor atmosphere of one or more pollutants in such quantities and of such duration that may tend to be injurious to human, plant, or animal life or property or which may unreasonably interfere with the comfortable enjoyment of life or property or the conduct of business [1, 2]. In this research work, emphasis will be on one of these criteria pollutants which is carbon monoxide because of the major threats it poses to human health. Carbon monoxide is a colourless, odourless, and highly poisonous gas produced in large quantities as a result of incomplete combustion of fossil fuels. It is known that the main source of carbon monoxide is from motor vehicle exhaust (vehicular emission); about two-thirds of the pol- lutant emissions come from transportation sources, while other sources include industrial processes and open burning activities [3, 4]. e natural concentration of carbon monoxide in air is around 0.2 ppm, and that amount is not harmful to humans, while exposure to the pollutant emission at 100 ppm or greater can be dangerous to human health. Carbon monoxide endangers humans specifically by its tendency to combine with haemoglobin in the blood. eir combination produces carboxyl haemoglobin (COHB), thus reducing the capacity of the blood to carry oxygen [5]. e acute effects produced by exposure to carbon monoxide (in parts per million) are given in Table 1. Probability models have been applied successfully in many physical phenomena such as wind speed, rainfall, river discharges, and air quality. It has been applied to fit the data of vehicular emission in Chennai, India, for predicting the con- centration of carbon monoxide in the ambient atmosphere [6, 7]. In their research, ten standard probability models were fitted to the data and goodness of fit was assessed using Kolmogorov-Smirnov test and Anderson-Darling test. Hindawi Publishing Corporation Journal of Probability and Statistics Volume 2014, Article ID 864965, 6 pages http://dx.doi.org/10.1155/2014/864965