Tag Archives: Air pollutants

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

Effects of Gas Flare From Utorogu Gas Plant on Biochemical Variables of Cassava Leaves (Manihot Esculentum), Delta State (Published)

Gas flaring is a major contributor to the emission of toxic gases and other gaseous pollutants into the atmosphere.  This study investigates the impact of gas flare on leaves of cassava around Utorogu gas plant, Delta State.  Three sampling locations were chosen at 1 km , 2km  and 3km distance from the gas flare stack and a control location  at Orerokpe. Ambient air quality was determined for methane (CH4) (ppm), oxide of sulphur (SOx) (ppm) oxide of nitrogen ( NOx) (ppm), carbon monoxide (CO) (ppm), and hydrogen sulphide (H2S) (ppm). Leaves collected were taken to the laboratory for analysis. Relative Leaf Water Content (RLWC)(%), Total Chlorophyll Content (TCC)(mg/m3), Leaf Extract pH( LEP)(mol/litre) and Ascobic Acid Content (AAC) (mg) were determined under standard laboratory methods.  Ensuing data were subjected to standard statistical analysis . Results showed that CH4 varied from 38.00-92.00ppm, H2S from 0.05-1.20ppm, CO from 11.00-26.40ppm, SO2 from 252.00-340.00ppm and NO2 from 82.00-190.00ppm. RLWC varied from 30.50-56.33, TCC varied from 1.98-4.66, LEP varied from 4.50-7.00mol/litre and AAC varied from 0.03-0.15. It was revealed that NOx, SOx and CO exceeded NESREA’s short-term tolerance limits for ambient air pollutants of (40-60) ppm, 100 ppm, and 10ppm respectively. This shows that air pollutants exerted significant inhibitory influence on biochemical activities of the leaf studied. Environmental regulatory agencies and oil exploration companies should help reduce gas flaring to avoid damages to crop production.

Keywords: Air pollutants, Crop growth, biochemical variables, cassava leaves

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

Effects Of Gas Flare From Utorogu Gas Plant On Biochemical Variables Of Cassava Leaves (Manihot Esculentum), Delta State (Published)

Gas flaring is a major contributor to the emission of toxic gases and other gaseous pollutants into the atmosphere.  This study investigates the impact of gas flare on leaves of cassava around Utorogu gas plant, Delta State.  Three sampling locations were chosen at 1 km , 2km  and 3km distance from the gas flare stack and a control location  at Orerokpe. Ambient air quality was determined for methane (CH4) (ppm), oxide of sulphur (SOx) (ppm) oxide of nitrogen ( NOx) (ppm), carbon monoxide (CO) (ppm), and hydrogen sulphide (H2S) (ppm). Leaves collected were taken to the laboratory for analysis. Relative Leaf Water Content (RLWC)(%), Total Chlorophyll Content (TCC)(mg/m3), Leaf Extract pH( LEP)(mol/litre) and Ascobic Acid Content (AAC) (mg) were determined under standard laboratory methods.  Ensuing data were subjected to standard statistical analysis . Results showed that CH4 varied from 38.00-92.00ppm, H2S from 0.05-1.20ppm, CO from 11.00-26.40ppm, SO2 from 252.00-340.00ppm and NO2 from 82.00-190.00ppm. RLWC varied from 30.50-56.33, TCC varied from 1.98-4.66, LEP varied from 4.50-7.00mol/litre and AAC varied from 0.03-0.15. It was revealed that NOx, SOx and CO exceeded NESREA’s short-term tolerance limits for ambient air pollutants of (40-60) ppm, 100 ppm, and 10ppm respectively. This shows that air pollutants exerted significant inhibitory influence on biochemical activities of the leaf studied. Environmental regulatory agencies and oil exploration companies should help reduce gas flaring to avoid damages to crop production.

Keywords: Air pollutants, Crop growth, biochemical variables, cassava leaves