The widespread informal settlements in Nairobi have interested many researchers and urban policymakers. Reasonable planning of urban density is the key to sustainable development. By using the spatial population data of 2000, 2010, and 2020, this study aims to explore the changes in population density and spatial patterns of informal settlements in Nairobi. The result of spatial correlation analysis shows that the informal settlements are the centers of population growth and agglomeration and are mostly distributed in the belts of 4 and 8 km from Nairobi’s central business district (CBD). A series of population density models in Nairobi were examined; it showed that the correlation between population density and distance to CBD was positive within a 4 km area, while for areas outside 8 km, they were negatively related. The factors determining population density distribution are also discussed. We argue that where people choose to settle is a decision process between the expected benefits and the cost of living; the informal settlements around the 4-km belt in Nairobi has become the choice for most poor people. This paper ends with suggestions for urban planning and upgrading informal settlements. The findings will increase our understanding of urban population distribution in underdeveloped countries.

Africa has been undergoing a rapid urbanization process, which is critical to the achievement of the 11th Sustainable Development Goal (SDG11). Using population density data from LandScan, we proposed a population density-based thresholding method to generate urban land and urban population data in Africa from 2001 to 2019, which were further applied to detect the spatiotemporal characteristics of Africa’s urbanization. The results showed that urban land and urban population have both grown rapidly in Africa, which increased by about 5.92% and 4.91%, respectively. The top three countries with the most intense urbanization process in Africa are Nigeria, the Democratic Republic of the Congo, and Ethiopia. The coupling relationship index of urban land expansion and population growth was 0.76 in Africa during 2001–2019. Meanwhile, the total proportion of uncoordinated development types at the provincial level was getting higher, which indicated an uncoordinated relationship between urban land expansion and population growth in Africa. Cropland, grassland, rural land, and forests were the most land-use types occupied by urban expansion. The proportion of cropland, grassland, and forests occupied was getting higher and higher from 2001 to 2019. The extensive urban land use may have an impact on the environmental and economic benefits brought by urbanization, which needs further research.

This study evaluates the performance of four bias correction methods based on CORDEX (coordinated regional climate downscaling experiment) domain six regional climate models (RCMs) at the Mara River Basin. A suitable bias correction method was considered to develop the future climate scenario. The performance of bias correction methods was evaluated by various statistical metrics based on the historical period and revealed that the distribution mapping (DM) techniques have strong performance under the different climatic conditions. The effectiveness of the DM method is found to be better at capturing the coefficient of variation and standard deviation of observed rainfall and temperature. Therefore, this study considers the future climate (2026–2095) from bias-corrected RCMs output using DM techniques. The results from bias-adjusted RCMs show an increase of rainfall (+118.3%) and temperature (+2.91) in the future climate under Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5. In addition, this study tested the random forest (RF) method to determine the capacity of each bias-corrected RCMs for reproducing the future rainfall and temperature under the RCP 4.5 and RCP 8.5 scenario. The results demonstrate that the RF can reproduce the climate variable with its average correlation (R2) of 0.93 for rainfall and 0.95 for temperature.

Africa’s PM_2.5 pollution has become a security hazard, but the understanding of the varying effects of urbanization on driven mechanisms of PM_2.5 concentrations under the rapid urbanization remains largely insufficient. Compared with the direct impact, the spillover effect of urbanization on PM_2.5 concentrations in adjacent regions was underestimated. Urbanization is highly multi-dimensional phenomenon and previous studies have rarely distinguished the different driving influence and interactions of multi-dimensional urbanization on PM_2.5 concentrations in Africa. This study combined grid and administrative units to explore the spatio-temporal change, spatial dependence patterns, and evolution trend of PM_2.5 concentrations and multi-dimensional urbanization in Africa. The differential influence and interaction effects of multi-dimensional urbanization on PM_2.5 concentrations under Africa’s rapid urbanization was further analyzed. The results show that the positive spatial dependence of PM_2.5 concentrations gradually increased over the study period 2000–2018. The areas with PM_2.5 concentrations exceeding 35 μg/m³ increased by 2.2%, and 36.78% of the African continent had an increasing trend in Theil–Sen index. Urbanization was found to be the main driving factor causing PM_2.5 concentrations changes, and economic urbanization had a stronger influence on air quality than land urbanization or population urbanization. Compared with the direct effect, the spillover effect of urbanization on PM_2.5 concentrations in two adjacent regions was stronger, particularly in terms of economic urbanization. The spatial distribution of PM_2.5 concentrations resulted from the interaction of multi-dimensional urbanization. The interaction of urbanization of any two different dimensions exhibited a nonlinear enhancement effect on PM_2.5 concentrations. Given the differential impact of multi-dimensional urbanization on PM_2.5 concentrations inside and outside the region, this research provides support for the cross-regional joint control strategies of air pollution in Africa. The findings also indicate that PM_2.5 pollution control should not only focus on urban economic development strategies but should be an optimized integration of multiple mitigation strategies, such as improving residents’ lifestyles, optimizing land spatial structure, and upgrading the industrial structure.

Background: Mara River Basin is an ecologically fragile area in East Africa, with a pattern of alternating wet and dry seasons shaped by periodic precipitation. Considering the regional biological traits and climatic change, the vegetation's response to seasonal variation is complicated and frequently characterized by time lags. This study analyzed the variation of the Normalized Difference Vegetation Index (NDVI) and investigated its time lag to precipitation at the monthly scale. NDVI characteristic peaks were proposed from the perspective of seasonal mechanisms and were quantified to assess the lag effect. Results: The results showed that the Anomaly Vegetation Index could identify low precipitation in 2006, 2009, and 2017. The NDVI showed an increasing trend in 75% of areas of the basin, while showed a decreased significance in 3.5% of areas, mainly in savannas. As to the time lag, the 1-month lag effect dominated most months, and the spatiotemporal disparities were noticeable. Another method considering the alternations of wet and dry seasons found that the time lag was approximately 30 days. Based on the time distribution of NDVI characteristic peaks, the average time lag was 35.5 days and increased with the range of seasons. Conclusions: The findings confirmed an increasing trend of NDVI in most regions from 2001 to 2020, while the trends were most obvious in the downstream related to human activities. The results could reflect the time lag of NDVI response to precipitation, and the 1-month lag effect dominated in most months with spatial heterogeneity. Four NDVI characteristic peaks were found to be efficient indicators to assess the seasonal characteristics and had a great potential to quantify vegetation variation.