Sewage and Organic Pollution Compounds in Nairobi River Urban Sediments Characterized by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT–ICR–MS)

Nairobi River sediments from locations adjacent to the Kawangware and Kiambio slums were analyzed via Fourier transform ion cyclotron resonance mass spectrometry with atmospheric pressure photoionization (APPI–FT–ICR–MS). The data from these ultrahigh resolution, untargeted measurements provided new insights into the impacts of local anthropogenic activity, which included likely benzo- and dibenzothiophene pollution with a suspected petrogenic origin, and prominent surfactant-like compositions. Other features in the data included highly abundant tetra-oxygenated compounds, and oxygenated nitrogen compounds with sphingolipid interpretations. Most notably, several hydrocarbon and oxygenated compound classes in the sediment data featured intensity patterns consistent with steroid molecular formulas, including those associated with sewage contamination investigatory work. In support of this interpretation, standards of cholesterol, β-sitosterol, stigmasterol, coprostanol, cholestanol, and 5α-sitostanol were analyzed via APPI, to explore steroid ionization behavior. Generally, these analytes produced radical molecular ions ([M]•+), and water-loss pseudo molecular ion species ([M–H2O]•+ and [M+H–H2O]+), among various other less intense contributions. The absence of pseudo molecular protonated species ([M+H]+) was notable for these compounds, because these are often assumed to form with APPI. The standard measurements demonstrated how steroids can create the observed intensity patterns in FT–ICR–MS data, and hence these patterns have the potential to indicate sewage contamination in the analysis of other complex environmental samples. The steroid interpretation for the Kawangware and Kiambio data was further verified by subjecting the steroid standard radical molecular ions to collision-induced dissociation and comparing the detected fragments to those for the corresponding isolated ions from a Kawangware sediment sample

Dissolved organic matter in continental hydro-geothermal systems: insights from two hot springs of the East African Rift valley

Little is known about the quantity and quality of dissolved organic matter (DOM) in waters from continental geothermal systems, with only a few reports available from the Yellowstone US National Park. In this study, we explored the chemodiversity of DOM in water samples collected from two geothermal hot springs from the Kenyan East African Rift Valley, a region extremely rich in fumaroles, geysers, and spouting springs, located in close proximity to volcanic lakes. The DOM characterization included in-depth assessments performed by negative electrospray ionization Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). Reduced, saturated and little aromatic DOM compounds were dominant in the hot spring waters collected from either the Ol Njorowa gorge (ON) or the south shore of the soda-saline Lake Elementaita (ELM). Oxygen-poor and sulfur-bearing DOM molecules prevailed in ON, probably reflecting abiotic sulfurization from sulfide-rich geofluids. Nitrogen-bearing aliphatic and protein-like molecules were abundant in ELM, possibly perfusing through the organic-rich sediments of the adjacent Lake Elementaita. Notably, the heat-altered DOM of ancient autochthonous derivation could represent an overlooked source of aliphatic organic carbon for connected lentic environments, with a potential direct impact on nutrient cycling in lakes that receive geothermal water inputs

Holocene bidirectional river system along the Kenya Rift and its influence on East African faunal exchange and diversity gradients

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Dommain, R., Riedl, S., Olaka, L. A., deMenocal, P., Deino, A. L., Owen, R. B., Muiruri, V., Müller, J., Potts, R., & Strecker, M. R. Holocene bidirectional river system along the Kenya Rift and its influence on East African faunal exchange and diversity gradients. Proceedings of the National Academy of Sciences of the United States of America, 119(28),(2022): e2121388119, https://doi.org/10.1073/pnas.2121388119.East Africa is a global biodiversity hotspot and exhibits distinct longitudinal diversity gradients from west to east in freshwater fishes and forest mammals. The assembly of this exceptional biodiversity and the drivers behind diversity gradients remain poorly understood, with diversification often studied at local scales and less attention paid to biotic exchange between Afrotropical regions. Here, we reconstruct a river system that existed for several millennia along the now semiarid Kenya Rift Valley during the humid early Holocene and show how this river system influenced postglacial dispersal of fishes and mammals due to its dual role as a dispersal corridor and barrier. Using geomorphological, geochronological, isotopic, and fossil analyses and a synthesis of radiocarbon dates, we find that the overflow of Kenyan rift lakes between 12 and 8 ka before present formed a bidirectional river system consisting of a “Northern River” connected to the Nile Basin and a “Southern River,” a closed basin. The drainage divide between these rivers represented the only viable terrestrial dispersal corridor across the rift. The degree and duration of past hydrological connectivity between adjacent river basins determined spatial diversity gradients for East African fishes. Our reconstruction explains the isolated distribution of Nilotic fish species in modern Kenyan rift lakes, Guineo-Congolian mammal species in forests east of the Kenya Rift, and recent incipient vertebrate speciation and local endemism in this region. Climate-driven rearrangements of drainage networks unrelated to tectonic activity contributed significantly to the assembly of species diversity and modern faunas in the East African biodiversity hotspot.R.D. was funded by a Smithsonian Human Origins Postdoctoral Fellowship and by Geo.X—the Research Network for Geosciences in Berlin and Potsdam. Fig. 1 D, E, and G and SI Appendix, Figs. S1 and S3 are based on the TanDEM-X Science DEM granted to L.A.O. and S.R. by the German Aerospace Center (DLR) in 2017. L.A.O. acknowledges the Volkswagen Foundation for funding this study with Grant No. 89369. M.R.S. and S.R. were supported by funds from Potsdam University and the Geothermal Development Company of Kenya, and R.B.O. and V.M. were supported by the Hong Kong General Research Fund. We acknowledge support from the National Museums of Kenya and the Kenya Government permission granted by the Ministry of Sports, Culture and the Arts, and by the National Commission for Science, Technology and Innovation (NACOSTI) Permits P/14/7709/683 (to R.P.) and P/16/11924/11448 (to L.A.O.). This work is a contribution of the Olorgesailie Drilling Project, for which support from the National Museums of Kenya, the Oldonyo Nyokie Group Ranch, the Peter Buck Fund for Human Origins Research (Smithsonian Institution), the William H. Donner Foundation, the Ruth and Vernon Taylor Foundation, Whitney and Betty MacMillan, and the Smithsonian Human Origins Program is gratefully acknowledged. LacCore is acknowledged for support in drilling and core storage

Impact of organic pollutants from urban slum informal settlements on sustainable development goals and river sediment quality, Nairobi, Kenya, Africa

The UN Sustainable Development Goals highlight the myriad of socio-economic and environmental challenges occurring as a result of anthropogenic chemical pollution. Urban sediments from informal settlements (slums) on the Nairobi, Ngong and Mathare Rivers (n = 25), were evaluated for sediment quality. Microtox bioassay identified 8 sites as toxic, 9 as moderately toxic and 8 as non-toxic. Slum sediments were characterised by high total organic carbon and Rock-Eval pyrolysis revealed bound carbon from a mix of raw sewage and domestic refuse. Sediments from Kiambio, Kibera, Mathare and Kawangware slums contained high coprostanol at 55–298 μg/g and epicoprostanol at 3.2–21.7 μg/g confirming appreciable incorporation of untreated human faeces. Hormones, antianalgeiscs, antiinflamatories, antiepileptics and antibiotics most affected Mathare > Kiambio > Kibera > Mukuru > Kawangware slums. Carbamazepine, ibuprofen, diclofenac and acetaminophen concentrations are amongst the highest reported in Kenyan river sediments and were positively correlated with faecal steroids (sewage). Common persistent organic pollutants, such as organochlorine insecticides ΣDDT 1–59 μg/kg, mean 21.2 μg/kg, Σ16PAH 182–2218 μg/kg, mean 822 μg/kg and Σ30 PCB 3.1–157.1 μg/kg, mean of 21.4 μg/kg were between probable effect likely and unlikely sediment quality guidelines (SQG). PAH source ratios and parent to alkyl-PAH distribution suggested vehicle exhaust, power stations (heavy oil), kerosene (cooking oil) and other pollution sources. Trace metal concentrations As, Cd, Cr, Hg and Ni were below SQG whereas Pb exceeded the SQG. This multi-contaminant characterisation of sediment quality in Nairobi supports the development and implementation of policies to improve urban infrastructure to protect ecological and human health. It demonstrates the need for environmental geochemists to engage in the science-policy interface associated with both global and national development frameworks, with particular reference to the Sustainable Development Goals, New Urban Agenda, and Kenya’s Vision 2030

Pharmaceutical pollution of the world's rivers

Environmental exposure to active pharmaceutical ingredients (APIs) can have negative effects on the health of ecosystems and humans. While numerous studies have monitored APIs in rivers, these employ different analytical methods, measure different APIs, and have ignored many of the countries of the world. This makes it difficult to quantify the scale of the problem from a global perspective. Furthermore, comparison of the existing data, generated for different studies/regions/continents, is challenging due to the vast differences between the analytical methodologies employed. Here, we present a global-scale study of API pollution in 258 of the world's rivers, representing the environmental influence of 471.4 million people across 137 geographic regions. Samples were obtained from 1,052 locations in 104 countries (representing all continents and 36 countries not previously studied for API contamination) and analyzed for 61 APIs. Highest cumulative API concentrations were observed in sub-Saharan Africa, south Asia, and South America. The most contaminated sites were in low- to middle-income countries and were associated with areas with poor wastewater and waste management infrastructure and pharmaceutical manufacturing. The most frequently detected APIs were carbamazepine, metformin, and caffeine (a compound also arising from lifestyle use), which were detected at over half of the sites monitored. Concentrations of at least one API at 25.7% of the sampling sites were greater than concentrations considered safe for aquatic organisms, or which are of concern in terms of selection for antimicrobial resistance. Therefore, pharmaceutical pollution poses a global threat to environmental and human health, as well as to delivery of the United Nations Sustainable Development Goals

Projected climatic and hydrologic changes to lake Victoria Basin Rivers under three RCP emission scenarios for 2015–2100 and impacts on the water sector

CITATION: Olaka, L. A., et al. 2019. Projected climatic and hydrologic changes to lake Victoria Basin Rivers under three RCP emission scenarios for 2015–2100 and impacts on the water sector. Water, 11(7):1449, doi:10.3390/w11071449.The original publication is available at http://www.mdpi.comRivers in the Lake Victoria Basin support a multitude of ecosystem services, and the economies of the riparian countries (Kenya, Tanzania, Uganda, Rwanda, and Burundi) rely on their discharge, but projections of their future discharges under various climate change scenarios are not available. Here, we apply Vector Autoregressive Moving Average models with eXogenous variables (VARMAX) statistical models to project hydrological discharge for 23 river catchments for the 2015–2100 period, under three representative concentration pathways (RCPs), namely RCPs 2.6, 4.5, and 8.5. We show an intensification of future annual rainfall by 25% in the eastern and 5–10% in the western part of the basin. At higher emission scenarios, the October to December season receives more rainfall than the March to May season. Temperature projections show a substantial increase in the mean annual minimum temperature by 1.3–4.5 °C and warming in the colder season (June to September) by 1.7–2.9 °C under RCP 4.5 and 4.9 °C under RCP 8.5 by 2085. Variability in future river discharge ranges from 5–267%, increases with emission intensity, and is the highest in rivers in the southern and south eastern parts of the basin. The flow trajectories reveal no systematic trends but suggest marked inter-annual variation, primarily in the timing and magnitude of discharge peaks and lows. The projections imply the need for coordinated transboundary river management in the future.https://www.mdpi.com/2073-4441/11/7/1449Publisher's versio

Analysis of Climate Variability and Trends in Southern Ethiopia

This study investigated the trends and variability of seasonal and annual rainfall and temperature data over southern Ethiopia using time series analysis for the period 1983–2016. Standard Anomaly Index (SAI), Coefficient of Variation (CV), Precipitations Concentration Index (PCI), and Standard Precipitation Index (SPI) were used to examine rainfall variability and develop drought indices over southern Ethiopia. Temporal changes of rainfall trends over the study period were detected using Mann Kendall (MK) trend test and Sen’s slope estimator. The results showed that the region experienced considerable rainfall variability and change that resulted in extended periods of drought and flood events within the study period. Results from SAI and SPI indicated an inter-annual rainfall variability with the proportions of years with below and above normal rainfall being estimated at 56% and 44% respectively. Results from the Mann Kendall trend test indicated an increasing trend of annual rainfall, Kiremt (summer) and Bega (dry) seasons whereas the Belg (spring) season rainfall showed a significant decreasing trend (p < 0.05). The annual rate of change for mean, maximum and minimum temperatures was found to be 0.042 °C, 0.027 °C, and 0.056 °C respectively. The findings from this study can be used by decision-makers in taking appropriate measures and interventions to avert the risks posed by changes in rainfall and temperature variability including extremes in order to enhance community adaptation and mitigation strategies in southern Ethiopia

Knowledge of climate change and adaptation by smallholder farmers: evidence from southern Ethiopia

Climate change has the greatest negative impact on low-income countries, which burdens agricultural systems. Climate change and extreme weather events have caused Ethiopia’s agricultural production to decline and exacerbated food insecurity over the last few decades. This study investigates whether farmers’ awareness and perceptions of climate change play a role in climate change adaptation using climate-smart agricultural practices. To collect data, 385 households in Southern Ethiopia were sampled using a multistage sampling. A Heckman probit two-stage selection model was applied to investigate the factors influencing farmers’ perceptions to climate change and adaptation measures through adoption of climate-smart agriculture practices, complemented with key informant interviews and focused group discussions. The results indicated that most farmers (81.80%) perceived that the local climate is changing, with 71.9% reporting increased temperature and 53.15% reporting decreasing rainfall distribution. Therefore, farmers attempted to apply some adaptation practices, including soil and water conservation with biological measures, improved crop varieties, agroforestry, improved breeds, cut and carry system, controlled grazing, and residue incorporation. The empirical results revealed that farmers adaptation to climate change through adoptions of CSA practices was significantly influenced by education, family size, gender, landholding size, farming experience, access to climate information, training received, social membership, livestock ownership, farm income and extension services. The study found that farmers’ perceptions of climate change and variability were significantly influenced by their age, level of education, farming experience, and access to climate information, hence, the need to focus on enhancing the accuracy of weather information, strengthening extension services, and considering a gender-sensitive adaptation approach toward improving farmers’ knowledge and aspirations. Agricultural policies should support the efforts of farmers to increase the reliance on climate risk and alleviate farmers’ difficulties in adopting climate-smart agriculture practices

A Transdisciplinary Framework for AI-driven Disaster Risk Reduction for Low-income Housing Communities in Kenya

This study was supported by the ADRELO project (reference: EP/V004867/1) funded by EPSRC/ UKRI under Belmont Forum. For more information, see https://adrelo-project.com/. URI - https://doi.org/10.1109/SMC52423.2021.9658957In the past 50 years, natural disasters worldwide have accounted for 2.06 million deaths and US$3640 billion in economic losses. These natural disasters are heavily influenced by the composite earth system processes and human interactions. In this paper, we focus our investigation to assess the impact of flooding in rivers and coastal regions and its impact on low-income communities. For this purpose, a transdisciplinary perspective from Artificial Intelligence (AI), Climate science, Socio-economics discipline is leveraged to map and identify their inter-relationships and challenges using Soft System Methodology (SSM). A transdisciplinary framework, named ADRELO1 Disaster Support System (ADSS), is therefore proposed to (1) identify the key parameters that can influence climate change, (2) stitch together a reusable multilayered transdisciplinary knowledge model, and (3) apply the observed multivariant data to AI-based algorithm to forecast climate change, analyze the impact of climate change on socio-economic outcomes and suggest potential disaster risk reduction actions. Research-based outcomes, from the given framework, will be used for policy prescription towards making flood-affected local communities self-resilient. ADSS will be applied first in a flood-prone region, such as Nyando in Kenya and Mozambique. It will then be extrapolated in other coastal regions of Florida and North-eastern Brazil to examine the applicability of the framework