Institute of Environmental Management and Quality Control

Permanent URI for this collectionhttps://dspace.njala.edu.sl/handle/20.500.144402/148

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    E-Waste Quantification and Machine Learning Forecasting in a Data-Scarce Context
    (Multidisciplinary Digital Publishing Institute, 2026-01-27) Mansaray, Abubakarr Sidique
    Quantifying e-waste in Sub-Saharan Africa remains constrained by scarce data, weak institutional reporting, and the dominance of informal sector activity. We present the first nationwide assessment of e-waste generation and Random Forest-based national forecasting in Sierra Leone. A mixed-methods survey administered 6000 questionnaires across all 16 districts, targeting households, institutions, enterprises, and informal actors. The study documented devices in use, storage, and disposal across the following six categories: ICT, appliances, lighting, batteries, medical, and other electronics. Population growth and device adoption simulations were combined with lifespan distributions and a Random Forest model trained on survey and simulated historical data to construct e-waste flows and forecast quantities through to 2050, including disposal fate probabilities for repurposing versus discarding. The results showed sharp spatial disparities, with Western Urban (Freetown) averaging about 10 kg per capita compared to 1.8 kg per capita in rural areas. Long-term district patterns were highly concentrated: 50-year annual averages indicated that Western Area Urban contributes 15.3% of national totals, followed by Bo (12.7%) and Western Area Rural (12.1%), with the top five districts contributing 59.1%. By 2050, total national e-waste entering reuse and disposal pathways was projected to reach 23.4 kilo tons per year (kt yr−1) with a 95% uncertainty interval (UI) of 11–42 kt yr−1 (and a 99% interval extending to 50 kt yr−1), corresponding to 0.9–3.4 kg/capita/year. Household appliances dominated total mass, ICT devices exhibited high reuse rates, and batteries showed minimal reuse despite high hazard potential. These findings provide critical evidence for e-waste policy, regulation, and infrastructure planning in data-scarce regions.
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    Utilizing Geospatial Tools to Assign Unique Identifiers to Water Bodies in a Low-Income Country
    (Scientific Research Publishing, 2021) Mansaray, Abubakarr Sidique
    Inland waters support the growth of several sectors including mining, agriculture, and health. This makes it crucial to have sustainable quantity and quality through conservation practices. Achieving sustainability requires information on the spatial distribution of water bodies. This requirement is particularly critical in low-income nations where dependence on natural resources is a key driver to economic growth. Unfortunately, these nations lack the resources to promote costly waterbody characterization. This study pre-sents a cost-effective approach in assigning Unique Identifiers (UIDs) that define locations and characteristics of rivers and streams. Our objective is to develop a scheme that can be used to identify and characterize rivers and streams in a nation. We utilized an open-source Digital Elevation Model (DEM) of NASA’s ASTER satellite and the hydrology tool in ArcGIS 10.7.1. The DEM was imported to ArcGIS followed by delineation of hydrologic regions, subregions, and stream orders. Each stream segment was given a UID based on its region and Strahler’s stream order system. We present a case study analysis for two regions within Sierra Leone using water quality data of selected rivers and streams. These will lay the foundation for a nationwide coding exercise and provide a useful reference for water resource practitioners.