http://197.159.135.214/jspui/handle/123456789/54 2026-04-04T05:33:05Z http://197.159.135.214/jspui/handle/123456789/1106 Fine particulate air pollution estimation in Ouagadougou using satellite aerosol optical depth and meteorological parameters Amooli, Joe Adabouk The main sources of fine particles in our cities are the exhausts of cars, trucks, buses, and offroad vehicles as well as other processes that involve the burning of fuels like wood, heating oil, or coal, and natural sources like dust storms from the Sahara Desert and forest fires. Fine particles tend to worsen conditions such as asthma, and other forms of respiratory diseases. Since the sources of these particles are a result of our daily activities it is urgent to measure their levels so we can plan outdoor activities safely and reduce their impact on our health. This work used satellite and weather information to develop four (4) models to help estimate Ouagadougou’s fine particles as a way of contributing to solving air pollution problems in African cities. The photo proves the sources of fine particles in the city of Ouagadougou and how residents breathe them without wearing air filters/nose marks. A Policy brief submitted to the West African Science Service Center on Climate Change and Adapted Land Use and Université Joseph KI-ZERBO, Burkina Faso in partial fulfillment of the requirements for the Master of Science Degree in Informatics for Climate Change 2023-09-04T00:00:00Z http://197.159.135.214/jspui/handle/123456789/1105 Agricultural soil characterization and crop recommendation using deep learning algorithms: Model Selection and AI Application Development Ali Abdou, Moussa Agriculture is vital, and soil is a fundamental component with unique characteristics for different crops. This thesis research aimed to develop a deep learning-based system for soil type classification. It explores the performances of eight CNNs architectures namely, DenseNet201, MobileNetV3Large, VGG16, VGG19, InceptionV3, ResNet50, Xception, and a novel architecture referred to as simple architecture, for classifying agricultural soil types found in Maradi, Niger. The research methodology encompasses data collection, cleaning, preprocessing, model building, hyperparameter optimization, model compilation, and the development of an AI-based application. The findings highlight that ResNet50 and DenseNet201 were better than other models for all performance metrics. Thus, the developed application is meant to empower farmers to optimize their practices in the face of land degradation and climate change challenges. A Policy Brief submitted to the West African Science Service Center on Climate Change and Adapted Land Use and Université Joseph KI-ZERBO, Burkina Faso in partial fulfillment of the requirements for the Master of Science Degree in Informatics for Climate Change 2023-09-01T00:00:00Z http://197.159.135.214/jspui/handle/123456789/1091 Assessing Bacteria Load And Antibiotic Resistance in The Gut of Two Fish Species Sold at Mindelo Fish Market and Polluted Water in Cabo Verde Ndure, Amie Fish is a vital protein source for billions worldwide, rich in essential fatty acids like omega-3s. Despite its nutritional benefits, it can harbor harmful bacteria, posing health risks if not handled or cooked properly. These bacteria can be native or non-native to fish. Research shows that the microbial communities in fish guts are influenced more by their environment than their species or diet. However, some studies suggest that the fish's species or diet can also impact these communities. Concern is growing about antibiotic-resistant microbes in aquatic environments, which can spread to humans and animals, leading to treatment challenges and higher health risks. A Policy brief submitted to the West African Science Service Center on Climate Change and Adapted Land Use and Universidade Técnica do Atlântico, Cabo Verde in partial fulfillment of the requirements for the Master of Science Degree in Climate Change and Marine Science 2023-09-01T00:00:00Z http://197.159.135.214/jspui/handle/123456789/1090 Using Remote Sensing Data to Monitor Primary Production in Cabo Verde: A Step Towards Smarter Ocean Management Assokpa, Kodjo Olivier More than 70% of the Earth's surface is covered by the ocean, making it the largest expanse (Costanza, 1999). The relationship between ocean ecosystems and global climate change is attracting much attention in recent years (Spring, 2019; Siswanto et al., 2020). As the base of the marine food web, phytoplankton plays a fundamental role in the biogeochemical cycling of elements by con-verting inorganic elements into organic compo-nents. Phytoplankton are advected microalgae and therefore dependent on marine currents. They can photosynthesize using light and nutrients such as NO3 (Nitrate), PO4 (Phosphate), Si (Silicate), Fe (Iron) and carbon dioxide (CO2) available in the water to produce oxygen and organic matter. As primary producers, many marine organisms, including fish, depend on them for their nutrition. They also contribute also to roughly half of the fixed carbon on the planet by absorbing atmospher-ic CO2 and producing 50% of dioxygen (O2) essen-tial for life on the earth’s surface (Martin, 2014). In the current scenario of climate change, a noticeable decrease in primary production on a larger scale has been observed. (Boyce et al., 2010). Chlorophyll-a (Chl-a) is a pigment found in algae and other photosynthetic organisms. Of all the types of chlorophyll, it is Chl-a that serves as a key indicator of phytoplankton biomass in the ocean (Behrenfeld & Falkowski, 1997). While estimating microscopic phytoplankton numbers and associated primary productivity is a significant challenge for ships (Dierssen & Randolph, 2012), satellite remote sensing of ocean color has become a reliable tool to study phytoplankton dynamics over various timescales (Vantrepotte & Mélin, 2009; Krasnopolsky et al., 2016). A Thesis submitted to the West African Science Service Center on Climate Change and Adapted Land Use and Universidade Técnica do Atlântico, Cabo Verde in partial fulfillment of the requirements for the Master of Science Degree in Climate Change and Marine Science 2023-09-01T00:00:00Z