This study presents a review of sources and atmospheric levels of anthropogenic air emissions in Nigeria with a view to reviewing the existence or otherwise of national coordination aimed at mitigating the continued increase. According to individual researcher's reports, the atmospheric loading of anthropogenic air pollutants is currently on an alarming increase in Nigeria. Greater concerns are premised on the inadequacy existing emission inventories, continuous assessment, political will and development of policy plans for effective mitigation of these pollutants. The identified key drivers of these emissions include gas flaring, petroleum product refining, thermal plants for electricity generation, transportation, manufacturing sector, land use changes, proliferation of small and medium enterprises, medical wastes incineration, municipal waste disposal, domestic cooking, bush burning and agricultural activities such as land cultivation and animal rearing. Having identified the key sources of anthropogenic air emissions and established the rise in their atmospheric levels through aggregation of literature reports, this study calls for a review of energy policy, adoption of best practices in the management air emissions and solid wastes as well as agriculture and land use pattern which appear to be the rallying points of all identified sources of emission. The study concluded that the adoption of cleaner energy policies and initiatives in energy generation and usage as against pursuit of thermal plants and heavy dependence on fossil fuels will assist to ameliorate the atmospheric loadings of these pollutants.

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Statistical modelling of hot smoke processing of pre-treated Tilapia (Oreochromis niloticus) fish was reportedly inaccurate, making it difficult to design, predict, and reproduce the finished product’s quality; hence, accurate modelling of this process is a gap in study. This study filled this gap and extended the literature by investigating the accuracy of artificial intelligent based model for the same process. Fuzzy inference system (FIS) model was developed using the already presented dataset in the literature from where inaccurate statistical models were reportedly derived. The dataset is on the effect of smoke temperature (80, 90 and 100OC) and smoke time (2.00, 2.50 and 3.00 h) on the gross energy value (GEV) (Kcal/g) and the overall acceptability (OA) properties of brined pre-solar dried and brined non-dried Tilapia (Oreochromis niloticus) fish. The efficiency of FIS membership function types (pimf, trimf and gbellmf) on the accuracy of the developed FIS model was also investigated. Coefficient of determination, root mean square error, individual percentage error and model accuracy were used to discern the model accuracy. Results showed that FIS had a modelling accuracy (𝑅2 value) between 0.9873 and 0.9999 as against 0.1072 and 0.5800 reported for the statistical model. The results suggested that FIS model outperformed the statistical model of Tilapia (Oreochromis niloticus) smoke processing and it is recommended for process/product design, control, and standardization.

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