Impact of Climate Change in Nigeria

Climate change is an adverse environmental phenomenon that is causing enormous concern all over the world. It refers to some anomalies in the climate system that is a result of human activities. These anomalies include increase in the concentration of GHGs, HFCs and CFCs in earth’s atmosphere, which will ultimately leadto global warming. In fact, global warming has already begun, as earth’s temperature has risen between 0.4 and 0.8°C in the last 100 years. Nigeria is one of the world’s most densely populated countries with a population of 180 million people, half of which are considered to be in abject poverty. Nigeria is recognized as beingvulnerable to climate change. Climate change and global warming if left unchecked will cause adverse effects on livelihoods in Nigeria, such as crop production, livestock production, fisheries, forestry and post-harvest activities, because the rainfall regimes and patterns will be altered, floods which devastate farmlands wouldoccur, increase in temperature and humidity which increases pest and disease would occur and other natural disasters like floods, ocean and storm surges, which not only damage Nigerians’ livelihood but also cause harm to life and property, would occur. The paper provides a strong starting point and a useful guide for furtherinvestigations and solution finding projects, both at the local and international levels which focus on more specific issues like public health, food security, energy, adaptations and barriers to them

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Simulation and Modeling of an Integrated Process Route for the Synthesis of Vinyl Chloride Monomer from Acetylene: Factorial Design Method and Artificial Neural Network

Vinyl Chloride gas is a nonirritating and colorless substance. It is usually colorless at a concentration lower than 3900 ppm (10,000 mg/m3). Vinyl Chloride is simply compressed to liquid for storage and shipping. At a concentration between 200 and 500 mg/m3, a Sweetish odor of Vinyl Chloride may be detected. This research paper is focused on the simulation of an integrated process route for the synthesis of Vinyl chloride Monomer from Acetylene via Aspen Hysys Simulation as well as the Factorial Design of the experiment with MINITAB 17.0. Fit Regression and Artificial Neural Network were employed for the modeling of the responses. Molar flow rates of acetylene (C2H2) and hydrogen chloride (HCl) predicts the conversions of acetylene and hydrogen chloride. A recycle unit is added to the process flow diagram and the maximum conversion of C2H2 and HCl is found to be 99.90 and 99.80 %, respectively. Analysis of variance (ANOVA) gives the results of the statistical correlation between the independent variables and response variables. The simulation and modeling results reveal that the Artificial Neural Network model gives better prediction and analysis of the process route with correlation coefficient (R squared values) of 97.921 % and 98.423 % for the conversion of C2H2 and conversion of HCl, respectively compared to the Factorial Design Method model with R squared values value of 79.47 % and 73.70 % for the conversion of C2H2 and conversion of HCl, respectively

Atmospheric dispersion modeling of uncontrolled gaseous pollutants (SO2 and NOX) emission from a scrap-iron recycling factory in Ile-Ife, Southwest Nigeria

In the last decade, government policies promoting foreign investments in the industrial sector particularly for small and medium scale enterprises in Nigeria have led to increased establishment of scrap-iron recycling factories in many states of the federation. Albeit the economic benefits in terms of waste material sourcing and job creation, these scrap-iron recycling factories have attracted significant public criticism due to the characteristic uncontrolled pollution (toxic gases) plume released from their operations into the atmospheric environment of host communities and thus aggravating existing and unresolved rural and urban air pollution problems in Nigeria. This study therefore provides model-based estimates of atmospheric dispersion for gaseous pollutants (SO2 and NOx) released from a scrap-iron recycling factory located in Ile-Ife, southwest Nigeria as a case study for investigating emission signature from such sources. Meteorological parameters measured at the factory location in 2012 and 2013 were used to execute U.S recommended short range (0.01°C/m), very weak winds (90%)