The need to study the variations of climate change in Nigeria becomes necessary at a time the universe and Nigerians, in particular are passing through challenges due to climate change as a result of emissions. The atmospheric gases have a greater transparency for incoming solar radiation, while the outgoing are trapped and re-emitted back to the Earth. This study correlated between global solar radiation and greenhouse gases over Nigeria using neural network. The results showed that positive correlations exist between solar radiations: CO2 and CH4 respectively, while exhibiting negative correlations with tropospheric ozone and water vapour. Consequently, an increase in 0.1017, 0.1350 units of CO2 and CH4, respectively could enhance the trapping and transmission of solar radiation in the atmosphere, while an increase of 1.1234 and 0.1530 units of tropospheric ozone and water vapour could cause absorption of solar radiation. The trapped energy is re-radiated back to the Earth, this warms up the atmosphere and the surface of the Earth resulting to global warming. Coefficient of determination revealed that 18%, 30%, 20%, and 29%, of the variances of solar radiation being studied is explained by the variance of the water vapour, tropospheric O3, CO2, and CH4, respectively.
The refractivity profile variation in troposphere is one of the aspects that influences long-distance terrestrial electromagnetic wave propagation and performance of communication systems. This study is aimed at calculating and estimating radio refractivity at Makurdi with tropospheric parameters of relative humidity, absolute temperature and atmospheric pressure using ITU-R and artificial neural network models. Validation results are thus, absolute temperature = 0.4313 K, relative humidity = 0.9989 %, pressure = 0.0201 (hpa) respectively. The validation of the correlation coefficient results shows that all the tropospheric parameters have effects on radio refractivity, but relative humidity has more effect which is attributed to the large quantity of moisture at the troposphere. From the estimation results, it is clear that artificial neural network has the capacity of estimating tropopheric refractivity since the estimated values has close agreement with the calculated values.
Atmospheric pollution due to carbon dioxide emission from different fossil fuels and deforestations are considered as a great and important international challenge to the societies. This study is to investigate carbon dioxide (CO2) distributions in selected points in Nigeria using neural network. Neural network model were used to estimate daily values of carbon dioxide, study spatial temporal variations of carbon dioxide, and study the annual variations of estimated and observed carbon dioxide in Nigeria. The study areas used in this work are thirty six (36) points location over Nigeria. The data used in this work is a satellite carbon dioxide () data were obtained from Global Monitoring for Environment and Security (GMES) under the programme of Monitoring Atmospheric Composition And Climate (MACC) www.gmes-atmosphere.eu/data between 2009-2014. The neural network architecture used comprises of three main layers; an input layer, a hidden layer and an output layer. Four input data were considered which include year, day of year (DOY) representing the time, latitude and longitude. Twenty hidden neurons were employed, while the output is the desired data of carbon dioxide. The results show that the increase in trend of CO2 in dry season in every part of the country is on yearly bases. In the wet season, the concentration of CO2 in Nigeria is not as much as in the dry season case, probably due to absorption of the gas by precipitation. The continuous annual increase of CO2 distribution suggests continuous increase of the greenhouse gas in Nigeria. This reveals continuous contribution of CO2 in Nigeria. The similarity in the estimated and observed signatures reveals that neural network model performance were excellent and efficient in determination of spatial distribution of CO2, thereby proving to be useful tool in modeling the greenhouse gases. The results show that neural network model has the capacity of investigating greenhouse gases variations in Nigeria.
Nitrogen dioxide emission is part of atmospheric pollutant that has been linked to climate change. Artificial neural network model were used to investigate nitrogen dioxide distributions in Nigeria at a selected points. The study areas used in this work are thirty six (36) points over Nigeria as shown in Fig. 1. The data used in this work is a satellite nitrogen dioxide () obtained from Global Monitoring for Environment and Security (GMES) under the programme of Monitoring Atmospheric Composition and Climate (MACC). The data used in this work is a satellite nitrogen dioxide data obtained from 2003-2014. The neural network processed the available data by dividing them into three portions randomly: 70% for the training, 15% for validation and the remaining 15% for testing. Input parameters were chosen to be latitude, longitude, day of the year, year. Observed nitrogen dioxide was inputted as targeted data, while the output nitrogen dioxide data were the estimated data. The results reveal that dry and wet season variations differ in Nigeria. Nitrogen dioxide concentrations were observed to be higher in the North during dry season, but were higher in the South during the wet season. This could imply that weather condition and seasons influences the concentrations and variations of nitrogen dioxide in Nigeria. The similar trend of the estimated and observed nitrogen dioxide of both diurnal and annual distributions suggests good performance of the model. The result shows that high concentrations of nitrogen dioxide contribute to climate change in Nigeria, resulting to global warming. Consequently, if left unchecked, increase in nitrogen dioxide may cause alteration in rainfall regimes and patterns, floods, and so on. These in turn will bring about adverse effects on livelihoods, such as crop production, livestock production, fisheries, forestry and post-harvest activities. Finally, we recommend analysis of nitrogen dioxide distributions in Nigeria to be regular.
Monthly average daily values of global solar radiation and sunshine hours over a period of five years (1987-1991) using artificial neural network were developed to predict global solar radiation at Minna which lies on latitude 09.37°N, longitude 06.32° and 265.4 m above sea level. The results were used to compare results from other researchers of different models in the same area. The correlation coefficient of our model was found to be 0.997. These values were found to be higher than the correlation coefficient of other models. The values from our model and other models were tested in terms of mean percentage error (MPE), mean bias error (MBE) and root mean square error (RMSE) and our model was found to have low error values when compared with other models. From the results of these studies, it was found out that all the models have predicting capacity, but our model has better results. This is being recommended for the prediction of global solar radiation for Minna and areas that have similar climate with Minna.
The study determined the variations of carbon dioxide and temperature within south-south and south-eastern parts of Nigeria from January 2009 to December 2014. The study specifically focused on the perceived impacts posed by climate change on environment within these regions due to carbon dioxide emissions. The results revealed that rise in temperature within these regions could significantly be dependent on the increase in CO2 emissions and other greenhouse gases. It was observed that CO2 emission increases continuously over all the years of study at each station. This could be attributed to high percent occurrences of urban warming experienced in these areas. The results also revealed that various impacts of climate change and weather within these regions could be due to high emission of carbon dioxide caused by fossil fuel, gas flaring etc found within these regions. It was also observed from the results that no gaseous pollutant or greenhouse gas can have 100% influences on climatic parameters like temperature.
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