Browsing by Author "Jimoda, L.A."
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Item Air quality impact of diesel back-up generators (BUGs) in Nigeria’s mobile telecommunication base transceiver stations (BTS)(Emerald Insight, UK, 2017) Adeniran, J.A.; Yusuf, R.O.; Amole, M.O.; Jimoda, L.A.; Sonibare, J.A.Purpose – The introduction of mobile telecommunication services in Nigeria led to the development of base transceiver stations (BTS) across the country. Inadequate power supply from the national grid has led to massive use of diesel-fueled back-up generators (BUGs). The purpose of this paper is to attempt to quantify and inform relevant stakeholders about air quality implications of BTS BUGs. Design/methodology/approach – Seven major telecommunication network operators were identified. Emission factor approach was used to estimate the quantity of important air pollutants such as NOx, CO, SO2, PM10, PM2.5, PAH and TVOC that are emitted from the use of the BUGs based on fuel consumption rate and generators’ capacity. Fuel-based emission inventory and emission factor from the United States Environmental Protection Agency AP-42 and National Pollution Inventory were used to estimate pollutants emission from diesel-powered generators used in the BTS sites and amount of diesel consumed. Land distribution and per capita dose of the estimated pollutants load were calculated. Findings – The study showed that the deployment of BUGs will lead to increase emissions of these air pollutants. The states that are most affected are Lagos, Kano and Oyo, Katsina and Akwa Ibom states with respective total air pollutants contribution of 9,539.61, 9,445.34, 8,276.46, 7,805.14 and 7,220.70 tonnes/yr. Originality/value – This study has estimated pollutant emissions from the use of diesel-fueled BUGs in mobile telecommunications BTS sites in Nigeria. The data obtained could assist in policy making.Item Ground level Concentration of some air pollutants from Nigeria thermal power plants(Taylor and Francis, 2016) Adesanmi, A.J.; Adeniran, J.A.; Fakinle, B.S.; Jimoda, L.A.; Yusuf, R.O.; Sonibare, J.A.Power sector in Nigeria is undergoing structural reforms aimed at improving and expanding the current generation capacity, using thermal power plants. Ground level concentrations of air pollutants emitted from natural gas-powered thermal power plants were estimated using the American Meteorological Society-Environmental Protection Agency Regulatory Model (AERMOD). The average 24-h ground level concentrations of CO, NOx, SO2, particulate matter (PM), and volatile organic compounds (VOCs) were 31.88–72.79; 61.33–104; 0.61–3.91; 0.21–1.52; and 0.19–1.09 ìg/m3, respectively. There is need for continuous monitoring of ground level concentration of pollutants around the thermal power plants to guarantee the safety of the environment in the host communities.Item Kinetics and neuro-fuzzy soft computing modelling of river turbid water coag-flocculation using mango (Mangifera indica) kernel coagulant(2018) Oke, E.O.; Araromi, D.O.; Jimoda, L.A.; Adeniran, J.A.This study investigates kinetics and Adaptive Neuro-Fuzzy Modeling (ANFM) of river turbid water coagulation-flocculation (CF) process using mango kernel coagulant (MKC). CF experiments were performed using jar test apparatus and the process kinetic-transport parameters (coagulation rate constant, half-life time, and particle diffusivity) were determined using kinetic- transport models. Grid-partitioning neuro-fuzzy programming codes were written and implemented in Matlab 9.2 software environment for the development of neuro-fuzzy architecture. The ANFM input data include initial water pH, initial water turbidity, biocoagulant dosage, CF time, and turbidity removal percentage (TRP) as output data. Generalized bell membership function was optimally selected for fuzzification of input variables and a hybrid algorithm was considered for the learning method of input-output data with constant output membership type. The minimum turbidity (0.51 NTU) of treated water was achieved at pH 12 and coagulant dosage of 2.5mg/L with coagulation rate constant, half-life (t1/2) and particle diffusivity 0.0194 s 1, 10.01 min, and 7.267 10 14 m2/s, respectively. The correlation coefficient (R2) between the experimental and neuro-fuzzy predicted values was 0.9924 and the ratio (K) of training error to testing error was 0.68. Thus, this study shows that ANFM can be used as a reliable tool for modeling river water CF and kinetic-transport parameter results are useful in process design, optimization, and control.