Soil erosion by water has accelerated over recent decades due to non-sustainable land use practices resulting in substantial land degradation processes. Spatially explicit information on soil erosion is critical for the development and implementation of appropriate Soil and Water Conservation (SWC) measures.The objectives of this study were to estimate the magnitude of soil loss rate, assess the change of erosion risk, and elucidate their implication for SWC planning in the Gobele Watershed, East Hararghe Zone, Ethiopia. Applying remote sensing data, the study first derived the Revised Universal Soil Loss Equation (RUSLE) model parameters in an ArcGIS environment and estimated the soil loss rates. The estimated total soil loss in the watershed was 1,390,130.48 tons in 2000 and 1,022,445.09 tons in 2016 with a mean erosion rate of 51.04 t ha⁻¹ y⁻¹ and 34.26 t ha⁻¹ y⁻¹, respectively. The study area was divided into eight erosion risk classes ranging from very low to extremely high. We established a change detection matrix of the soil erosion risk classes between 2000 and 2016. The change analysis results have revealed that about 70.80% of the soil erosion risk areas remained unchanged, 19.67% increased in total area, and 9.53% decreased, showing an overall worsening of the situation. We identified and mapped areas with a higher and increasing erosion risk as SWC priority areas using a Multi-criteria Decision Rules (MCDR) method. The top three priority levels marked for the emergency SWC measures account for about 0.04%, 0.49%, and 0.83%, respectively. These priority levels are situated along the steep slope areas in the north, northwest, south, and southeast of the Gobele Watershed. It is, thus, very critical to undertake proper intervention measures in upslope areas based on the priority levels to establish sustainable watershed management in the study area.

Urban growth in sub-Saharan Africa presents significant challenges to sustainable development, food security, and environmental conservation. The rapid urban expansion and impact on agricultural land reduction in central Ethiopian metropolitan areas (Addis Ababa and Sheger city) exemplify these issues while simultaneously offering opportunities for sustainable development. This study aims to quantify and characterize the spatiotemporal dynamics of urban expansion in Addis Ababa and the surrounding Sheger city, explicitly focusing on understanding the impact of urban expansion on farmlands. The supervised random forest (RF) classification in the Google Earth Engine platform was used to prepare land use and land cover (LULC) for 1990, 2000, 2010, and 2023. The study employed an analytical framework incorporating multiple methodologies: intensity analysis at interval, categorical, and transitional levels to quantify urban growth trajectories; gradient direction and distance analyses to examine spatial expansion patterns; and Land Expansion Index (LEI) and Landscape Dynamic Typology (LDT) metrics to characterize the urban morphology and spatial dynamics of the study area. The results revealed that edge expansion is the predominant mode of urban development, primarily affecting farmlands in the eastern section. Built-up areas quadrupled between 1990 and 2023, whereas arable land declined. Intensity analysis revealed significant changes, particularly affecting farmlands. Our LDT analysis showed reduction in stable areas and increased in LULC changes from 1990 to 2023. The findings highlight the need for revised urban development strategies in Ethiopia to focus on compact and efficient growth while safeguarding agricultural lands, aligning with SDGs 2, 11, and 15 to promote balanced development that ensures urban and agricultural sustainability.

Water erosion is accelerating soil loss rates in the East Hararghe Zone due to inappropriate human activities and their complex and intertwined interactions with natural factors, particularly in sensitive agroecosystems that lack soil and water conservation (SWC) measures. Although these dynamic processes cause prolonged impacts, a comprehensive assessment of the risk of soil erosion has not yet been undertaken at the zonal level. To bridge this gap, we employed the revised universal soil loss equation (RUSLE) prediction model, along with remote sensing and geographic information systems (GIS), to estimate annual soil erosion rates, analyze the temporal-spatial patterns of erosion risk, and evaluate the potential of standard conservation practices to reduce soil loss in croplands. Total soil erosion (in millions of tonnes/year; Mt yr⁻¹) was estimated to be 9 in 1990, 14 in 2000, 12 in 2010, and 11 in 2020, with average rates of 33, 50, 44, and 39 t ha⁻¹ yr⁻¹, respectively. This suggests an overall 18% increase in soil erosion from 1990 to 2020. Over 75% of the area showed a tolerable soil loss rate (120 t ha⁻¹ yr⁻¹), which accounts for more than 80% of soil loss, making SWC planning a priority. Analysis of land-use land-cover change (LULCC) confirmed a higher increase in soil loss for LULCC that involved conversion to croplands, with average rates of 36.4 t ha⁻¹ yr⁻¹ (1990–2000), 70 t ha⁻¹ yr⁻¹ (2000–2010), and 36 t ha⁻¹ yr⁻¹ (2010–2020). The results have further revealed that implementing supportive practices such as terracing, stripping, and contouring could reduce average soil erosion by approximately 87%, 65%, and 29%, respectively, compared to the baseline model’s prediction. Therefore, a rigorous cost–benefit analysis is essential to design and implement optimal location-specific practices that maximize investment returns in SWC efforts and ecological restoration. However, we acknowledge the limitations of this study, associated with an empirical model that does not account for all forms of erosion, as well as reliance mainly on secondary data, which may affect the accuracy of the predicted outcomes.

Air pollution is a critical twenty-first century environmental and public health challenge that is linked to millions of deaths and ecological harm. Accurate prediction of pollutants such as PM₁₀ is vital for mitigation and urban sustainability. This study combines geospatial modeling with three machine learning algorithms (MLAs), Random Forest (RF), K-Nearest Neighbor (KNN), and Naïve Bayes (NB), to identify PM₁₀ hotspots in Addis Ababa, Ethiopia. PM₁₀ data from 11 stations (August 2021–August 2023) were analyzed alongside 25 atmospheric, climatic, anthropogenic, and pollution source predictors. A concentric zonal approach was used to assess spatial variability across radial distances and directional sectors and was supported by 30 m-resolution satellite imagery, climate data, and local geospatial repositories. The model accuracies were 0.95 (KNN), 0.93 (RF), and 0.88 (NB), with distinct performance trade-offs: RF predicted the largest “Good” PM₁₀ zones (78.98 km²), KNN highlighted the most “UnHealSen” areas (279 km²), and NB predict “Moderate” coverage (311 km²). High PM₁₀ concentrations clustered in eastern and northwestern sectors, aligning with industrial zones and traffic density. The results demonstrate the efficacy of MLAs and geospatial integration in producing high-resolution pollution maps. We advocate for targeted emission controls in hotspots, expanding public transit to reduce vehicular emissions, and incorporating air quality metrics into urban planning. This study advances air quality assessment methods for rapidly urbanizing regions, providing data-driven strategies to combat pollution and enhance ecological resilience in African cities.