Abstract: Urban expansion, a type of LCLU change that leads to substantial gains in urban/built-up areas at the expense of green or open spaces, is a common occurrence in developing countries. Documentary review was used to carry out a thorough examination of the pace of urban growth and its effects on agricultural land. Landsat images had to be retrieved, preprocessed, and their accuracy assessed before being used to calculate the trend of LCLU change. The findings for the first objective illustrated that between the years 2001-2011-2016-2019-2022, the urban area has grown at an estimated annual average rate of 53%, while the vegetation area witnessed an overall sharp decline of 22%, mostly; due to a tremendous augmentation in urban area. The findings for the second objective indicated that Agricultural land area has faced an overall reduction rate of approximately 27%; population growth being the main driver for this reduction. This population growth, together with the emigration of settlers from Kigali city to Runda sector, is the main driver of this increase. The findings for the third objective illustrated that there is a clear linkage between urban growth and agricultural land degradation in Runda sector. Basing on the results of this study, there is actual urge for further public backing of the vertical construction of homes and companies to preserve green space. Farmers still need to be instilled how to make the most of a small plot of land in order to produce more. Moreover, supplementary policy implications are also required in order to accommodate a delegated responsibility that is environment-conscious.Abstract: Urban expansion, a type of LCLU change that leads to substantial gains in urban/built-up areas at the expense of green or open spaces, is a common occurrence in developing countries. Documentary review was used to carry out a thorough examination of the pace of urban growth and its effects on agricultural land. Landsat images had to be retrieved, p...Show More
Abstract: The oil industry has a relevant role in the generation of Greenhouse Gases (GHG) in its various segments, among them the Exploration and Production of Oil and Natural Gas (E&P). There are several methodologies for GHG inventories, each with different degrees of uncertainty, which makes the quantification of emissions complex, given the large number of variables to be analyzed. According to the Compendium of Greenhouse Gas Emissions Methodologies for the Oil and Gas Industry of the American Petroleum Institute (API), all GHG emissions should be calculated as a product of an "activity factor" by an appropriate "emission factor". That is, the amount of fuel used, considering how it is used. The product between the activity data and the emission factors provides an estimate of the GHG emissions associated with the company's activities. Based on this premise, this paper presents a model developed in System Dynamics (SD) for the preparation of inventories of CO2 and CH4 emissions, the main GHG emitted by the oil industry. The model was developed to meet the requirements of "Subpart W" of the United States Environmental and Protection Agency (USEPA) CFR Part 98, which states that oil and gas E&P facilities that emit at least 25 x 103 t CO2e/year, must report their estimates of total annual GHG emissions, annual individualized emissions of each GHG, and annual individualized emissions of each GHG broken down by source type expressed in metric tons of CO2e. The proposed model goes beyond the USEPA requirements in that it also allows estimation of emissions of CO2, of CH4 and their equivalence in CO2e from specific sources and groups of sources, generating an estimate of the emissions profile over the entire lifetime of the inventoried facility.Abstract: The oil industry has a relevant role in the generation of Greenhouse Gases (GHG) in its various segments, among them the Exploration and Production of Oil and Natural Gas (E&P). There are several methodologies for GHG inventories, each with different degrees of uncertainty, which makes the quantification of emissions complex, given the large number...Show More
Abstract: The water from the Abengourou dam is used for the production of drinking water. This study aims to characterise this resource in order to assess the quality of the water for monitoring and sustainability of the operation. Six sampling points were identified, four of which were at the main entrances, one inside the water body and another at the dam. Samples were taken at three different depths and during the two main seasons. In total, some forty physical, chemical and microbiological parameters were monitored for the thirty-six samples taken in one year. The results obtained show an inter-seasonal variation in the parameters monitored. The waters of the dam are very weakly mineralised and not very turbid. The highest turbidity during the wet and dry seasons are respectively 6.5 NTU and 11.1 NTU. The electrical conductivity of the water is between 173 and 190 µS/cm in the dry season and between 149 and 163 μS/cm in the wet season. This is mainly due to rising water levels during the wet season and evaporation during the dry season. Concentrations of iron, pesticide residues, manganese and ortho-phosphate above the WHO guidelines for drinking water were recorded. Average iron levels in the wet and dry seasons are 0.84 and 0.53 mg/L respectively. Manganese levels reached 2.02 in some samples in the dry season. Organohologenes were found at levels up to 0.04 µg/L in the high-water period. A greater presence of germs indicating faecal contamination was found during the high-water period. This contamination is of human or mixed human-animal origin depending on the sampling point. The highest levels are recorded at point P2, which represents the urbanised part of the catchment area for several of the parameters analysed.Abstract: The water from the Abengourou dam is used for the production of drinking water. This study aims to characterise this resource in order to assess the quality of the water for monitoring and sustainability of the operation. Six sampling points were identified, four of which were at the main entrances, one inside the water body and another at the dam....Show More