Please use this identifier to cite or link to this item: http://ir.library.ui.edu.ng:8080/jspui/handle/123456789/1190
Title: DETERMINATION OF PROBABILITY DISTRIBUTIONS FOR MODELLING AIR POLLUTANTS FROM VEHICULAR EMISSIONS IN LAGOS STATE
Other Titles: A DISSERTATION IN THE DEPARTMENT OF STATISTICS SUBMITTED TO THE FACULTY OF SCIENCE IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF PHILOSOPHY OF THE UNIVERSITY OF IBADAN
Authors: OLUGBODE, M. A.
Keywords: Vehicular emission variability
Survival and hazard rates
Probability
Issue Date: 2014
Abstract: Statistical analysis of emission data in the literature has been mostly limited to description of air pollutants variability. The use of probability distribution for modelling emission data needed for environmental quality management has received little attention. Appropriate probability distribution to model air pollutants is necessary for better understanding of emission variability. This study was aimed at determining probability distributions for modeling air pollutants from vehicular emissions in Lagos State. Secondary Data from Lagos State Traffic Management Agency were collected for Imota, Maryland, and Ikotun and Oshodi, representing low, medium and high vehicular traffic areas respectively. The data collected was between May 2007 and April 2008 on the selected sample locations. The data covers air pollutants from vehicular emission of Total Suspended Particulate (TSP), Particulate Matter 10 (PM10), Particulate Matter 2.5 (PM2.5), Carbon (II) Oxide (CO), Sulphur (IV) Oxide (SO2), and Nitrogen (IV) Oxide (NO2). Commonly used positively skewed probability distributions (PDs) were investigated using standard distribution software to select the PDs that best describe the data on each pollutant. Mean and standard deviation were used to characterise the distributions. Kolmogorov-Smirnov test statistic was used for testing the goodness-of-fit of the distributions. National Environmental Standards and Regulations Enforcement Agency (NESREA) air quality standard rates were inserted into the probability density function of the selected distributions. Each of the examined air pollutants from vehicular emissions had different probability distributions for describing variability of vehicular emission in Lagos State. Johnson S.B. (3P) (p<0.0003) was found suitable for modeling TSP, Beta (p<0.003) for PM10, Pearson 6 (4P) (p<0.0005) for PM2.5, Log-logistics (3P) (p<0.0035) for SO2, Pearson 5 (3P) (p<0.0024) for NO2 and CO had no specific distribution that fit. The mean concentration of TSP, PM10, PM2.5, CO, SO2 and NO2 were 585.0±363.6 µg/m3, 285.2±193.2 µg/m3, 150.4 ±108.4 µg/m3, 2.9±0.8 ppm, 27.6±9.7 ppbv and 31.1±30.0 ppbv respectively. The skewness values were 1.5, 1.3, 2.3, 0.8, 0.2, and 1.4 while the kurtosis values were 3.2, 2.9, 7.6, 0.004, 1.0 and 1.1. The test statistics for the skewness and kurtosis are 0.2≤G≤1.5 and 0.7≤Z≤8.3. The mean of TSP and PM10 were above the standard while the mean of CO, SO2 and NO2 were below. But, the mean of PM2.5 is equal to that of the standard value. The hazard rates of the air pollutants from vehicular emission were 0.9, 0.7, 0.4, 0.1 and 0.1 respectively, while the survival rates are 0.1, 0.3, 0.6, 0.9 and 0.9 respectively. The appropriate distributions for modelling selected air pollutants from vehicular emission in Lagos State were determined. Measures of the hazard and survival rates in relation to vehicular emission have been provided. Implementation and enforcement of environmental laws on air pollutants of vehicular emission must be taken with all seriousness and enforced towards the safety of life of all and sundry.
URI: http://ir.library.ui.edu.ng:8080/jspui/handle/123456789/1190
Appears in Collections:Academic Publications in Statistics

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