Telesphore Kabera

Pharmaceuticals, pesticides, and polyphenols are increasingly detected in aquatic environments, impacting water quality and aquatic life. This study screened these contaminants in Rwanda’s Nyabugogo River, analysing 45 water samples from rural, peri-urban, and urban areas using solid-phase extraction and LC–MS/MS. Fifty-seven pharmaceuticals, five pesticides, and four polyphenols were quantified, with the highest average concentrations of albendazole (960 ng/L), metalaxyl (70 ng/L), and caffeic acid (49 ng/L). Pharmaceutical levels were significantly higher in urban areas, reflecting inadequate sanitation and wastewater management in Kigali, while pesticide and polyphenol concentrations varied across different types of land use, influenced by agricultural and urban discharges. Temporal sampling revealed significant within-day concentration fluctuations but consistent day-to-day levels, indicating persistent contamination sources. These findings provide essential baseline data for monitoring organic pollutants and informing strategies to mitigate contamination in urban river systems.

Flood susceptibility mapping is crucial for understanding flood-prone areas and mitigating the associated risks in vulnerable regions like the Sebeya Catchment. This study adopted a GIS-based Analytical Hierarchy Process (GIS-AHP) integrated with local community knowledge to evaluate flood susceptibility using 10 conditioning factors: Topographic Wetness Index (TWI), Elevation, Rainfall, Slope, Land use/Land cover (LULC), Soil types, Normalized Difference Vegetative Index (NDVI), Distance to roads, Distance to rivers, and drainage density. These factors were selected based on their established influence on flood susceptibility as identified through literature review, expert consultation, and local community experience in the flood-affected zones. Spatial datasets were gathered from remote sensing platforms, Digital Elevation Models, Meteorological records, and existing geospatial databases, and were processed within a GIS environment. The pairwise comparison matrix of the AHP was used to derive weighting coefficients representing the relative contribution of each factor in inducing flood, with Rainfall (0.23), Slope (0.15), Distance to river (0.12), drainage density (0.12), and Elevation (0.11) as the most influential criteria. The findings revealed that 88.4% of the study area falls within a moderate flood-susceptible zone, whereas 6.4% and 5.2% fall within high and low susceptible zones, respectively. The current study indicates that damage to infrastructure, loss of livelihoods, displacement of communities, and increased costs of disaster response are key consequences observed in affected regions. A confusion matrix approach was employed to validate the flood susceptibility map, and the results indicate 0.97 as an overall accuracy, confirming strong model performance and reliability. The proposed adaptive strategies for enhancing flood resilience include improvement in land use planning, use of early warning systems, and sustainable catchment management.

Flood susceptibility mapping is crucial for understanding flood-prone areas and mitigating the associated risks, particularly in vulnerable regions like the Sebeya Catchment. This study has adopted a GIS-AHP approach integrated with local community knowledge over flood susceptibility factors such as Topographic Wetness Index (WTI), Digital Elevation Model (DEM), Precipitation, Slope, Land Use/ Land Cover (LULC), Normalized Vegetative Index (NDVI), Distance to Roads, Distance to River, and Drainage Density. The pairwise comparison matrix was used to determine each factor's weight according to its influence in inducing flood. The findings revealed that 33.1% of the total area has a very and high susceptibility to floods, whereas the rest part of the catchment is moderately susceptible to floods. Most social economic activities in this study are located in high-risk zones, which significantly to appearance of flooding impacts. Current study indicates that, damage to infrastructure, loss of livelihoods, displacement of communities, and increased costs of disaster response are key consequences observed in affected regions. A confusion matrix approach was employed to validate the flood susceptibility map, and the results indicate an overall accuracy of 0.92, confirming strong model performance and reliability. The study further proposes adaptive strategies and provides recommendations for enhancing flood resilience, including improvement in land-use planning, use of early warning systems, and sustainable catchment management. Further studies should develop an economic-loss prediction model based on flood-susceptibility mapping.