Malawi Journal of Science and Technology

Environmental health effects and biosurfactant production potentials of microorganisms isolated from five petroleum stations in Fate, Ilorin, Nigeria

David Olugbenga Adetitun — 2024-11-07

Air harbors large quantity of bacteria and fungi which makes it imperative to carry out microbiological analysis of outdoor air. In this study the microbiological quality of air of five fuel stations at Fate, Ilorin were assessed. The isolated bacteria were tested for their ability to produce biosurfactant. Settling plate technique was employed for sample collection using nutrient agar plates and potato dextrose agar plates for bacterial and fungal isolation. The bacterial counts in the selected locations ranged from 3.72 ×10² cfu/m³ to 1.6592×10⁴ cfu/m³, while the fungal count ranged from 1.57 × 10² cfu/m³ to 1.18×10³ cfu /m³. The highest bacterial and fungal count was observed at station five which was the busiest during the sampling
period. Seven bacterial isolates and five fungal isolates were identified. The microorganisms identified include Bacillus sp, Lactobacillus sp, Micrococcus sp and Corynebacterium sp as the bacteria isolates while Aspergillus sp, Alternaria alternata, Fusarium oxysporum and Rhizopus oryzae as the fungi isolates. Bacillus sp and Aspergillus niger had the highest microbial occurrence at 100% all through the sampling period. The Corynebacterium sp and Bacillus coagulans isolated exhibited biosurfactant producing potential that can be further tested for bioremediation purposes. The opportunistic pathogenicity of the bacterial and fungal species isolated in this research has vividly demonstrated that the microflora of the fuel stations at Fate, Ilorin, has public health implications. It is therefore recommended that proper sanitation and hygiene should be observed by everyone who associates with the petroleum stations environment.

Socio-economic factors affecting the uptake of fish solar tent dryers in the Lake Chilwa Basin

Herbert R. Nyirenda — 2024-11-07

This study investigates the socio-economic factors influencing the adoption of fish solar tent dryers in the Lake Chilwa basin, Malawi. The research employs a mixed- methods approach, combining qualitative and quantitative data collection techniques. Data were gathered through focus group discussions with 25 participants and surveys administered to households in Kachulu and Nchenga, targeting adopters and non-adopters of fish solar tent dryers. The analysis included logistic regression to assess the impact of socio-economic characteristics, such as age, sex, marital status, education, access to savings accounts, extension services, and fish processing training, on the uptake of fish solar tent dryers. The findings reveal that fish processed with fish solar tent dryers are of higher quality, cleaner, have a longer shelf life, and command better market prices than traditional methods. Access to extension services and training significantly increases adoption rates. However, economic challenges hinder widespread adoption, particularly the high costs of constructing and maintaining fish solar tent dryers. The study concludes that enhancing extension services and providing financial support can significantly improve the uptake of fish solar tent dryers, thereby promoting sustainable fisheries management and improving the economic status of fish processors in the Lake Chilwa basin.

Spatial and temporal dynamics of urban green spaces: an assessment using remote sensing time-series data in Blantyre City, Malawi

Emmanuel Chinkaka — 2024-11-07

Urban expansion and its ecological footprint are increasing at an alarming rate globally. This is putting pressure on the management of Urban Green Space (UGS) which are vital for biodiversity and ecological conservation. UGS contribute to sustainable development of these urban ecosystems. Recently, UGS have been considered to be substantially important for quality of life. In this study, UGS in the city of Blantyre, which has experienced rapid urbanization from 1990 to 2020, were delineated using remotely sensed Landsat-5 TM and Landsat-8 OLI time-series imagery. Maximum Likelihood Supervised Learning Algorithm was utilized to characterize landcover/landuse (LULC) categories, and further extrapolate to dynamic patterns of Urban Green Spaces (UGS). Kappa statistical coefficient and overall accuracy assessment were used to validate the LULC classification. Post-classification technique was used to compare and empirically categorize LULC and UGS changes between 1990 and 2020. Vector analysis change detection was performed to assess the dynamic patterns in UGS over time. The results indicate
rapid decrease in UGS footprint by 19.26km 2 representing a 42% decrease between 1990 and 2020. These changes in the study area are attributed to increased urbanization, population growth, socio-economic development, changes in microclimatic patterns and lack of policy and enforcement by the city authorities. The finding that the UGS in Blantyre city has substantially decreased over the past three decades is significant to the city’s policymakers, residents and researchers to better understand the shifting dynamics in LULC, and the particularities of UGS
depletion in such a critical city for Malawi’s socio-economic growth.

MODIS NDVI assessment of forest degradation in the federal capital territory Abuja, Nigeria, Sub Saharan Africa: A case study of the year 2000 – 2022

Akus Kingsley Okoduwa — 2024-11-07

The Federal Capital Territory (FCT) Abuja is one of Africa's fastest-growing cities, and its rapid, unsustainable growth has led to forest degradation. This study aims to assess changes in Abuja's forest cover from the year 2000 to 2022 by analyzing annual averages of the Normalized Difference Vegetation Index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The data extraction and processing were conducted via JavaScript code editor on the Google Earth Engine (GEE) platform and ArcGIS 10.7.1. The user's accuracy (UA), producer's accuracy (PA), overall accuracy (OA), and kappa coefficient (KC) of the
classified NDVI maps were computed via ArcGIS 10.7.1 and Google Earth Pro. The OA for 2000 and 2022 were 90% and 93%, respectively. The KC were 0.85 and 0.90, respectively. In the year 2000, the maximum value of the NDVI was 0.713, while the minimum value was -0.01. On the other hand, in the year 2022, the maximum value decreased to 0.634, while the minimum value increased to 0.05. A reclassified map was created using the NDVI threshold from the raster image. The NDVI threshold value was able to identify areas with no vegetation, sparse vegetation, or dense vegetation, which are the land cover classes considered in this study. In 2000, areas with no vegetation occupied 8 km², which later increased to 53.2 km² in 2022. Sparse vegetation occupied an area of 3,411 km², which later increased to 6,905 km² in 2022. Furthermore, in the year 2000 dense vegetation occupied an area of 3,936 km², which later decreased to 388 km² in the year 2022, which indicates massive forest degradation. The findings of this study have practical applications in the fields of environmental monitoring and forest management in FCT, Abuja, enabling policymakers to promote sustainable development.