Ground level concentrations of hydrocarbon emissions from diesel fuelled electric power generators

Abstract

ISC-Aermod view dispersion model has been used to study the ground level concentration of hydrocarbon (HC). The purpose was to predict the air quality effects from off-grid diesel power generators operated by a textile factory in Lagos, Nigeria on its host airshed. Emissions from 22 point sources in 5 sections of the factory were considered with 6 different scenarios. 4 years of hourly meteorological observations were used for the investigation of dispersion. The model output showed the highest value of maximum ground level concentration at 90 m south east of the factory. The predicted impact using the worst case scenario showed that the ambient HC of the host air shed will change by 0.01-0.05% of Nigeria's Federal Ministry of Environment (FMENV) standard. HC emissions from factory will not significantly affect the host air shed as the maximum concentrations from the worst case scenario were still lower than the national standard. However, since emissions from other factories will also be released into the same host environment, an integrated approach factoring the contributions from other factories should be employed in host air quality management.

Ambient Air Emission Profiles of Polycyclic Aromatic Hydrocarbons around a Typical University Power House in Nigeria

“…A very glaring example is the deployment of backup diesel generators at the several thousand base transceiver stations of mobile telecommunication industries in Nigeria (Okedere et al, 2018 Okedere and. Apart from economic considerations regarding the cost of diesel, environmental sustainability associated with burning fossil fuels for energy generation is a crucial factor that has been alluded to in previous studies (Fakinle et al, 2013;Okedere et al, 2015;Okedere and Elehinafe, 2016;Fakinle et al, 2018a). Energy production systems such as diesel-fueled internal combustion engines contribute significantly to the emission of pollutants to the environment (Fakinle et al 2018b;Lala et al, 2019).…”