Contribution of Vehicular Emissions to Climate Change in Nigeria: A Closer Look (Published)
Climate change is fast becoming a global challenge, as most countries in the world have continue to increase their industrial capacities, resulting in increase in greenhouse gas emissions in the atmosphere. Also, the increase in human population in Nigeria has caused increase in motor vehicles, thereby resulting to release of more gaseous pollutants to the atmosphere. This study examined contributions of vehicular emissions to climate change in Port Harcourt, Nigeria. The concentration of air particulate matter and pollutants were monitored in three locations (Rumuokoro, Rumuola and Ada George) selected on the basis of traffic density. Air quality was monitored with MX6 Ibrid Multi gas monitors, MET ONE GT 321 for particulate matter, Davis Vantage Vue Weather Station for metrological parameters. Data on meteorological factors such as air temperature, wind speed, relative humidity were collected from the nearest weather station in Port Harcourt. Traffic records were taken at the designated locations using a close circuit television (Plate 5.1) in the morning, afternoon and evening respectively. All the parameters were monitored in each location, five days in a week every month for two years (2016-2017). The result of the analysis revealed that there was an increase in concentration of average air pollutants across the areas monitored between 2016 and 2017. The variation was attributed to increase in vehicular traffic volume and change in climatic conditions. This implies that emission rate from vehicles will continue to increase and contribute significantly to climate change, except measures are put in place to mitigate it.
Citation: Ucheje O.O. and Okolo O.J. (2023) Contribution of Vehicular Emissions to Climate Change in Nigeria: A Closer Look, International Journal of Environment and Pollution Research, Vol.11, No.1 pp.43-62
Keywords: Air pollutants, Climate Change, concentration level, traffic volume, vehicular emission
Modeling of CO and PM2.5 Concentration Level in High Traffic Density Areas, Using Regression Model, (Published)
The study modeled air pollutants concentration level in selected high traffic density areas of Port Harcourt, Nigeria. It also investigated the factors that affect the concentration of air pollutants and the effects of vehicular density on the daytime, seasonal and annual patterns. This was with the view to ascertain the pattern of air pollutant concentrations in high traffic density areas of the city. Traffic records were taken at the designated locations by counting the number of vehicles passing through a point for two hours in the morning, afternoon and evening, using a close circuit television (Plate 5.1). All the parameters were monitored in each location once every month (Monday to Friday) for two years (2016-2017). Data obtained were analyzed using ANOVA and multiple linear regressions, where appropriate. Results obtained revealed that the concentration of CO and PM2.5varied significantly (p < 0.05) between 2016 and 2017. Traffic volume was found to contribute significantly to the concentration of the air pollutants while meteorological factors such as temperature, humidity and wind speed had significant effects on their dispersion. The study concluded that significant relationship exists between the daytime pattern in vehicular volume and air pollutants concentration in the study area. Also, there was an increase in concentration of average air pollutants across the areas monitored between 2016 and 2017. The variation was attributed to increase in vehicular traffic volume. Furthermore, concentration of air pollutants varied at different degrees of temperature, humidity and wind speed. A model was developed to predict the concentration of CO and PM2.5 at various metrological factors and vehicular volume.
Keywords: Air pollutants, Modeling, Traffic, concentration level, vehicular volume
Characterization and Modelling Of Air Pollutants Transport from Panteka Market, Jimeta-Yola, Nigeria (Published)
The primary motivation of the current research was to apply Land GEM model to predict gaseous pollutant mobility by means of pollutant concentrations, annual waste mass received, and dumpsite open year from the research area. Land GEM model is believed to have wide application on emission rates from landfills/dumpsites using both site specific and default model parameters. Emission concentration levels were achieved through field and laboratory experimental work from vegetable waste dumpsites using scientific calibrated instruments. Data obtained were applied on Land GEM computer based software; version 3.02 in order to predict air-pollutant transport from the market environment and her surroundings. The model was tested to ascertain its validity where the measured and simulated values indicated good match with an error of 3.8% .The closure year of the case study dumpsite A was predicted to be in 2074 having reached hazardous level in 2024 while control dumpsite B predicted a closure year of 2023 and hazardous level in 2019 with modeling efficiency of 64%. Understanding the types of gases emitted from decomposing vegetable waste dumpsites (CH4, CO2, NMOC, H2S) and their transport pattern could go a long way to ensuring control measures of these pollutions there by having a sustainable zero wastes market to boost economic activities under pleasant environment; hence healthy environment is a prerequisite of healthy life, and fighting pollution is definitely the best way of healthy life.
Keywords: Air pollutants, Dumpsites, Land GEM, Model, Transport
Prediction and Modeling of Seasonal Concentrations of Air Pollutants in Semi-Urban Region Employing Artificial Neural Network Ensembles (Published)
This study utilizes Artificial Neural Network (ANN) ensembles to predict seasonal variation of air pollutants in semi-urban region of Eleme, Rivers state, Nigeria. A ten year monthly concentrations of SO2, NO2, CO and CH4 in the region was obtained for dry and rainy seasons. Air pollutant concentrations in semi urban area of Eleme can be attributed mainly to industrial activities, vehicular emissions and some local background concentrations influenced by meteorological and geographical conditions of the area. Training of the network models was achieved using Neural NetTime Series feature of MATLAB software. Observed concentrations of pollutants and meteorological parameters were used as input variables for the prognostic models. The developed ANN prognostic models accurately captured the dynamic relationships between pollutant concentrations and meteorological predictor variables. The relationships between predicted and observed values were highly significant at 95% of confidence level for all models as dry and rainy seasons models gave R2 greater than 0.99 (indicating close relationships between observed and predicted values). CH4 showed R2 of 0.9946 and 0.9974 for dry and rainy seasons respectively; CO showed R2 of 0.9918 and 0.9972 for dry and rainy seasons respectively; NO2 showed R2 of 0.9998 and 0.9982 for dry and rainy seasons respectively; SO2 showed R2 of 0.9921 and 0.9991 for dry and rainy seasons respectively. The trend in predicted pollutants indicated that the study area is a major receptor of air pollutants emanating mainly from industrial activities and vehicular exhaust emissions. Further research study is needed to compare ANN model with other modeling approaches such as with multiple linear regression models for the prediction of air pollutants.
Keywords: Air pollutants, Artificial Neural Network, Hidden Layer, Input layer, Output Layer., Semi-Urban Region