A multilayer perceptron neural network approach for optimizing solar irradiance forecasting in Central Africa with meteorological insights.

Promoting renewable energy sources, particularly in the solar industry, has the potential to address the energy shortfall in Central Africa. Nevertheless, a difficulty occurs due to the erratic characteristics of solar irradiance data, which is influenced by climatic fluctuations and challenging to regulate. The current investigation focuses on predicting solar irradiance on an inclined surface, taking into consideration the impact of climatic variables such as temperature, wind speed, humidity, and air pressure. The used methodology for this objective is Artificial Neural Network (ANN), and the inquiry is carried out in the metropolitan region of Douala. The data collection device used in this research is the meteorological station located at the IUT of Douala. This station was built as a component of the Douala sustainable city effort, in partnership with the CUD and the IRD. Data was collected at 30-min intervals for a duration of around 2 years, namely from January 17, 2019, to October 30, 2020. The aforementioned data has been saved in a database that underwent pre-processing in Excel and later employed MATLAB for the creation of the artificial neural network model. 80% of the available data was utilized for training the network, 15% was allotted for validation, and the remaining 5% was used for testing. Different combinations of input data were evaluated to ascertain their individual degrees of accuracy. The logistic Sigmoid function, with 50 hidden layer neurons, yielded a correlation coefficient of 98.883% between the observed and estimated sun irradiation. This function is suggested for evaluating the intensities of solar radiation at the place being researched and at other sites that have similar climatic conditions.

Persistent organic pollutants in sediments of the Wouri Estuary Mangrove, Cameroon: Levels, patterns and ecotoxicological significance.

The anthropogenic impact in the Wouri Estuary Mangrove located in the rapidly developing urban area of Douala, Cameroon, Africa, was studied. A set of 45 Persistent Organic Pollutant were analysed in surficial mangrove sediments at 21 stations. Chlorinated Pesticides (CLPs), Polychlorinated Biphenyls (PCBs) and Polycyclic Aromatic Hydrocarbons (PAHs) have concentrations ranging from 2.2 - 27.4, and 83 - 544 ng/g, respectively. The most abundant CLPs were endosulfan, alachlor, heptachlor, lindane (γ-HCH) and DDT, which metabolites pattern revealed recent use. Selected PAHs diagnostic ratios show pyrolytic input predominantly. The sum of 7 carcinogenic PAHs (ΣC-PAHs) represented 30 to 50% of Total PAHs (TPAHs). According to effect-based sediment quality guidelines, the studied POPs levels imply low to moderate predictive biological toxicity. This study contributes to depict how far water resources are shifting within what is now termed the Anthropocene due to increasing local pressures in developing countries or African countries.