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Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS)
ISSN:2141-7016
| Abstract: This study was carried out to identify the existing gaseous pollutants emitted from Ughelli East gas plant in Delta State, examine their concentrations as well as their impacts on the environment. The study is very significant since it helps the environmental engineers and scientists in their professional responsibilities in terms of urban planning and human habitation; provides vital information to decision makers and aid governmental and corporate decision making in management and control of environmental pollution. Eleven monitoring points were identified, using spacing distances of 60m, 80m, 100m, 150m, 200m, 250m, 300m, 350m, 400m, 450m and 500m from the flare point. Volatile organic compounds (VOCs), methane (CH4), nitrogen dioxide (NO2), sulphur dioxide (SO2) and ozone (O3) were detected and their concentrations obtained using Multi-parameter gas monitor for a period of three months. Fine particulate matter (PM2.5) was also detected and the concentration obtained using SPM meter for a period of three months. Thermo Anemometer was used to obtain the values of wind speed, ambient temperature, atmospheric pressure and relative humidity, which affect the dispersion of gaseous pollutants. The geospatial coordinates of the flow station were obtained using Global Navigation Satellite System (GNSS) receivers; Artificial Neural Network (ANN) software was used to predict the concentration of the gaseous pollutants and validate the obtained field data. The obtained maximum concentrations of the gaseous pollutants in the study area are47.800µg/m3, 6.730µg/m3, 0.340µg/m3, 47.140µg/m3, 0.051µg/m3, and 0.047µg/m3for VOCs, CH4,NO2,PM2.5, O3 and SO2 respectively. The Pollution Standard Index (PSI) values computed are within the range of 0-50 indicating good environment, hence the study area was declared non-hazardous. |
| Keywords: gaseous pollutants, pollutants’ concentration , pollution standard index, air quality modelling, artificial neural network |
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