Ambient concentrations of fine particulate matter (PM2.5) in sub-Saharan African cities like Nairobi can vary significantly due to the distribution and intensity of local and regional emission sources. We assess the spatiotemporal variability of PM2.5 in Nairobi using low-cost sensor and reference instrument data from urban background (2020-2022) sites and from several source-specific sites (June to December 2021). To our knowledge, this work represents the longest and most spatially differentiated dataset for this city. Data from urban background sites demonstrates seasonal variation driven by precipitation. PM2.5 concentrations were higher during the warm-dry (JF, 17.1-18.8 mu g m-3) and cool-dry (JJAS, 21.0-25.5 mu g m-3) seasons and lower during the rainy seasons of MAM (14.8-17.0 mu g m-3) and OND (13.2-17.2 mu g m-3). Seasonal differences are systematic, and larger than the inter-annual variability. Our analysis of source-specific PM2.5 measurements (June to December 2021) reveals for the more polluted JJAS season the highest PM2.5 recorded at traffic/residential sites (28.8-29.1 mu g m-3), followed by urban background (23.3-24.1 mu g m-3) and suburban background (22.5 mu g m-3). The traffic/residential impacted sites demonstrate noticeable morning and evening peaks associated with traffic and residential emissions, while diurnal profiles for urban background and sub-urban background sites remain flat during the day but display noticeable evening peaks, pointing again to the impact of residential emissions. At the urban background site and during the JJAS season, an additional midday peak is probably related to residential cooking emissions.

Introduction Air pollution is linked with poor neurodevelopment in high-income countries. Comparable data are scant for low-income countries, where exposures are higher. Longitudinal pregnancy cohort studies are optimal for individual exposure assessment during critical windows of brain development and examination of neurodevelopment. This study aims to determine the association between prenatal ambient air pollutant exposure and neurodevelopment in children aged 12, 24 and 36 months through a collaborative, capacity-enriching research partnership.Methods and analysis This observational cohort study is based in Nairobi, Kenya. Eligibility criteria are singleton pregnancy, no severe pregnancy complications and maternal age 18 to 40 years. At entry, mothers (n=400) are administered surveys to characterise air pollution exposures reflecting household features and occupational activities and provide blood (for lead analysis) and urine specimens (for polycyclic aromatic hydrocarbon (PAH) metabolites). Mothers attend up to two additional antenatal study visits, with urine collection, and infants are followed through age 36 months for annual neurodevelopment and caregiving behaviour assessment, and child urine and blood collection. Primary outcomes are child motor skills, language and cognition at 12, 24 and 36 months, and executive function at 36 months. The primary exposure is urinary PAH metabolite concentrations. Additional exposure assessment in a subset of the cohort includes residential indoor and outdoor air monitoring for fine particulate matter (PM2.5), carbon monoxide (CO), ultrafine particles (UFP) and black carbon (BC).Ethics and dissemination This study was approved by the Kenyatta National Hospital - University of Nairobi Ethics and Research Committee, and the University of Washington Human Subjects Division. Results are shared at annual workshops.

This article reports the development of a novel embedded waveguide ultrasonic sensor for detecting the onset of damage in reinforced concrete structures. A sleeved waveguide is proposed to confine guided ultrasonic waves in one-dimension, with leakage to the surrounding media only through specially created openings, thus reducing attenuation losses and enhancing the capability to inspect large structures from a single transducer location. The test frequency and mode are identified through modelling, and the interaction of leaky guided ultrasonic waves with delamination within the concrete volume is studied. Numerical simulations validated by experiments are used to study the changes in wave features such as mode velocity, wavelength and wave reflection in the delamination region, helping to estimate its location. Further simulation studies are carried out to demonstrate the possibility of using multiple waveguide sensors and sleeve openings to provide a full view of the concrete volume. The results are encouraging for practical long-range and large-scale monitoring of concrete volumes using the proposed sleeved waveguide ultrasonic sensors.

Maternal PM2.5 exposures in informal settlements in Nairobi exceeded WHO air quality targets, with low-quality cooking fuel use identified as the most important non-ambient source.

A Low-cost sensors for particulate matter can provide high spatiotemporal resolution monitoring of air quality, especially in much of the Global South, and sub-Saharan Africa (SSA) in particular, where reference-grade instrumentation is often not available. However, ensuring high-quality data from low-cost sensor (LCS) platforms is essential. Until now, LCS required calibration by collocation with a reference-grade monitor to be used for more than qualitative studies of air quality, but reference-grade monitors are not available in many countries of the Global South. Since a key artifact in optical PM sensors is aerosol hygroscopic growth, we explore the viability of an alternative LCS calibration method: a hygroscopic growth correction factor using particle composition data from the MERRA-2 reanalysis dataset. We compare 3 different LCS located in 3 different areas of SSA – Kenya, Ghana, and South Africa - with 3 different calibration techniques: traditional linear calibrations with a reference-grade monitor, a κ-Köhler-derived correction with MERRA-2 data, and a random forest machine learning regression utilizing MERRA-2 and the regulatory-grade monitor. Random forest regressions using MERRA-2 particle composition data and collocation with a reference-grade monitor improve sensor performance to near that of regulatory-grade monitors. But even without collocation, a hygroscopic growth correction based on MERRA-2 particle composition alone can improve LCS PM2.5 performance by reducing mean-normalized bias to near-zero and reducing error by up to 40%.

Detection of defects located close to design features such as welds and supports remains a challenge in guided ultrasonic wave inspection, primarily due to the diffraction limit. Although metamaterial based approaches hold promise, the best previous work in this regard required placing a sensor right above the defect location to achieve resolution. Here, a novel angled channel metamaterial concept is proposed to overcome this limitation, thus permitting placing of a sensor at an offset from the defect location. The concept is demonstrated and discussed using simulations validated by experiments. It is shown that sub-wavelength resolution of crack-like defects is possible using the angled channel metamaterial offset by a distance of up to half the operating wavelength. The physics of this problem is further discussed using simulations and analysis, bringing out the strengths and limitations of the proposed technique, highlighting the benefits for guided wave screening of hidden regions.

Air pollution in Africa is a significant public health issue responsible for 1.1 million premature deaths annually. Sub-Saharan Africa has the highest rate of population growth and urbanization of any region in the world, with substantial potential for future emission growth and worsening air quality. Accurate and extensive observations of meteorology and atmospheric composition have underpinned successful air pollution mitigation strategies in the Global North, yet Africa in general and East Africa in particular remain among the most sparsely observed regions in the world. This paper is based on the discussion of these issues during two international workshops, one held virtually in the United States in July 2021 and one in Kigali, Rwanda, in January 2023. The workshops were designed to develop a measurement, capacity building, and collaboration strategy to improve air quality-relevant measurements, modeling, and data availability in East Africa. This paper frames the relevant scientific needs and describes the requirements for training and infrastructure development for an integrated observing and modeling strategy that includes partnerships between East African scientists and organizations and their counterparts in the developed world. SIGNIFICANCE STATEMENT: Air pollution is a leading environmental risk factor in East Africa that is expected to worsen with rapid urbanization and economic growth occurring in the region. The unique emission sources will impact atmospheric composition and chemistry and are of significant current interest to understand their impact on climate and air quality mitigation efforts everywhere. There is a need to quantify emission trends from different regions of the world and develop reliable methods for inventories. Relationships between scientists from both the Global North and the Global South will help to advance and implement measurements and to build global atmospheric chemistry capacity.

The paucity of fine particulate matter (PM2.5) measurements limits estimates of air pollution mortality in Sub-Saharan Africa. If well calibrated, low-cost sensors can provide reliable data especially where reference monitors are unavailable. We evaluate the performance of Clarity Node-S PM monitors against a Tapered element oscillating microbalance (TEOM) 1400a and develop a calibration model in Mombasa, Kenya’s second largest city. As-reported Clarity Node-S data from January 2023 through April 2023 was moderately correlated with the TEOM-1400a measurements (R2 = 0.61) and exhibited a mean absolute error (MAE) of approximately 7.03 µg m–3. Employing three calibration models, namely, multiple linear regression (MLR), gaussian mixture regression (GMR) and random forest (RF) decreased the MAE to 4.28, 3.93, and 4.40 µg m–3 respectively. The R2 value improved to 0.63 for the MLR model but all other models registered a decrease (R2 = 0.44 and 0.60 respectively). Applying the correction factor to a 5-sensor network in Mombasa that was operated between July 2021 and July 2022 gave insights to the air quality in the city. The average daily concentrations of PM2.5 within the city ranged from 12 to 18 µg m–3. The concentrations exceeded the WHO daily PM2.5 limits more than 50% of the time, in particular at the sites nearby frequent industrial activity. Higher averages were observed during the dry and cold seasons and during early morning and evening periods of high activity. These results represent some of the first air quality monitoring measurements in Mombasa and highlight the need for more study.

Carbon monoxide (CO) concentrations in the troposphere are decreasing globally, with Africa as an exception. Yet, the region is understudied, with a deficit of ground-based observations and highly uncertain CO emission inventories. This paper reports multiyear observational CO data from the Mt. Kenya Global Atmosphere Watch (GAW) station, as well as summertime CO isotope observations from both Mt. Kenya and Nairobi, Kenya. The CO variability at Mt. Kenya is characterized by slightly increased concentrations during dry periods and a strong influence of short-term pollution events. While some data gaps and differences in instrumentation complicate decadal-scale trend analysis, a small long-term increase is resolved. High-pollution events are consistent with isotopic signal from downwind savanna fires. The isotope fingerprint of CO in Nairobi indicates an overwhelming dominance (near 100 %) of primary emissions from fossil fuel combustion with implications for air pollution policy. In contrast, the isotope signature of CO intercepted at the large-footprint Mt. Kenya region suggests that at least 70 % is primary sourced, with a predominance likely from savanna fires in Africa. Taken together, this study provides quantitative constraints of primary vs. secondary CO in the eastern Africa region and in urban Nairobi, with implications for satellite-based emission inventories as well as for chemical transport and climate modeling.

This paper investigates the use of a citizen science approach for air quality monitoring to explore the likely pollution impacts of the new Missing Link #12 road passing through the informal settlement of Kibera, within Nairobi. Citizen science approaches are gaining relevance in air quality monitoring thanks to the advancement in environmental monitoring technology and the opportunities created for community-based organizations to collect data on air pollution through low-cost sensors. Fourteen households located in proximity to the Missing Link#12 were equipped with optical particle sensors. Data collected indicated that people living along the road are exposed to levels of PM2.5 and PM10 above WHO recommendations, mainly due to the particulate generated by the construction site and fuels used for indoor cooking. A community engagement workshop revealed that participatory approaches are useful for improving awareness of air pollution and associated health implications. It also allowed the community to enhance their capability to gain and use scientific tools to address local issues, and potentially lobby decision-makers to solve them. In the context of transport infrastructure development in African cities, such an approach can be a means of collecting data and monitoring the impacts of air pollution during and after road building.

Anthropogenic activities in cities can be major sources of fine particulate matter which contribute significantly to increased mortality and disease. In rapidly developing cities of eastern Africa, lack of routine air pollution measurements have hampered formulation of actionable air quality policies. This study integrates ground-based observations of low-cost sensors (LCS) and regional chemical transport modelling (CHIMERE, https://www.lmd.polytechnique.fr/chimere/) to quantify spatial-temporal variability of PM2.5 and NO2 concentrations, primary/secondary aerosol loading, local versus regional pollution share, and contribution of key economic sectors. Prior to deployment, LCS PM2.5 mass concentrations were calibrated with a reference instrument (BAM-1020), while LCS NO2 measurements could only be normalized internally. Between June-December 2021 period, sensors were deployed at urban background site (IPA, and UoN), urban traffic sites (KUCC, BuruBuru, and Marurui), and a peri-urban site (Ngong). BuruBuru and Marurui are in addition exposed to nearby residential emissions. Daily average PM2.5 varied from 26.3 to 27.6 µg/𝑚3 at traffic sites, 17.8 to 21.7 µg/𝑚3 at urban background sites, while it was 20.3 µg/𝑚3 at peri-urban site. PM2.5 and NO2 diurnal patterns mimicked daily traffic cycle with constantly higher evening peaks compared to morning peaks indicating residential emissions. A link of “large pollution” events with PM2.5 concentrations above 50 µg/m3 and low wind speeds (<4 m/s) was made evident and points to local sources. Preliminary modelling results of a nested CHIMERE run over Eastern Africa down to 2 km horizontal resolution show satisfying results when compared to measurements. They point to a strong urban source of fine particle pollution, with the strongest mass contribution of primary organic aerosol. Analysis of final model output will help to better understand air quality dynamics in Nairobi and ultimately help evaluation of possible future emission mitigation scenarios.

In Kenya, cases of malnutrition and nutritionally related ailments have been on the rise. This calls for the search for food crops with vital trace elements. The aim of the current study was to use energy dispersive X-ray fluorescence spectroscopy (EDXRF) to determine the concentrations of Zn, Fe, Cu, and Mn in the stems and leaves of the African spider plant (Cleome gynandra) in the context of nutrition. Stems and leaves of the African spider plant grown in the highland and in the lowland of Molo Ward, Nakuru, Kenya, were found to contain total available Zn in the range of 140 +/- 50 to 230 +/- 60 mg/kg, Fe in the range of 2200 +/- 700 to 3900 +/- 1700 mg/kg, Cu at 13 +/- 3 to 16 +/- 5 mg/kg, and Mn at 380 +/- 120 to 400 +/- 140 mg/kg. There was no significant difference in the average concentrations of the trace elements in the leaves and stems of the African spider plant from the lowland and highland regions of Molo Ward (p > 0.05). The concentrations of Zn, Fe, Cu, and Mn in the African spider plants is high enough to make it a nutritious traditional vegetable. Therefore, farmers should be encouraged and empowered to grow more vegetables.

Taking holistic actions to improve urban air quality is central to reducing the health risks associated with urbanisation, yet local evidence-based and institutional frameworks to achieve this are still challenging especially in many low-and middle-income countries (LMICs). This paper develops and applies an integrated systemic approach to explore the state of air quality management in Nairobi, Kenya; as an LMIC exemplar city. The urban diagnostics approach developed assesses current particulate matter air pollution in Nairobi; quantifies anthropogenic emissions for the years 2015 and 2020 and projects scenarios of impacts of actions and inactions to 2030. This was combined with a review of grey literature on air quality policies, urban development and interviews with key stakeholders. The analysis suggests that commendable progress has been made to improve air quality in Nairobi but continuing hazardous levels of air pollution still require concerted policy efforts. Data available for numerical simulations have low spatial resolution and are generated from global emission inventories that can miss or misrepresent local emission sources. The current air quality data gap that needs to be addressed are highlighted. Strong political support is required to ensure that current air quality improvement approaches are evidence based to achieve long-term sustainability goals.

In Kenya, where malnutrition and hidden hunger still pose a significant challenge to growth and development, beans are an essential source of food for most of the population. However, data on micronutrient in beans are limited, and although they are required in very small amounts, they are essential for growth and development of plants as well as human beings. The aim of this study was to determine the concentrations of Mn, Fe, Cu, and Zn, in common beans in Kenya. Samples of both fresh bean leaves and dry grains of the most common bean varieties were collected from small-scale farmers in Muguga and Kyevaluki in Kiambu and Machakos Counties, respectively. They were analyzed using total reflection x-ray fluorescence (TXRF), a technique of increasing interest for food analysis since it is fast, easy, and reliable. Standard methods of sample preparation were used, and average elemental concentrations were compared with established sufficiency ranges. Bean leaves and dry bean grains from both sampling areas had sufficient concentrations of the four analyzed elements. Except for the Fe concentrations, the concentrations of the others elements were in the lower end of the sufficiency range for all bean species. The results obtained from this study are essential information for both farmers and policy makers and can be incorporated in programs to guide policy aimed at improving the nutritional quality of beans and thus food security in Kenya.

Rapid urbanization and population growth drives increased air pollution across Sub-Saharan Africa with serious implications for human health, yet pollutant sources are poorly constrained. Here, we analyse fine particulate aerosol concentrations and radiocarbon composition of black carbon over a full annual cycle in Nairobi, Kenya. We find that particle concentrations exceed the World Health Organisation’s recommended safe limit throughout the year, with little seasonal variability in particle concentration or composition. Organics (49 ± 7%) and water-soluble inorganic ions, dominated by sulfates (13 ± 5%), constitute the largest contributors to the particle loadings. Unlike large cities on other continents, the fraction of black carbon in particles is high (15 ± 4%) suggesting black carbon is a prominent air pollutant in Nairobi. Radiocarbon-based source quantification indicates that fossil fuel combustion emissions are a dominant source of black carbon throughout the year (85 ± 3%). Taken together, this indicates that black carbon emissions from traffic are a key stressor for air quality in Nairobi.

Abstract. Urban conurbations of East Africa are affected by harmful levels of air pollution. The paucity of local air quality networks and the absence of capacity to forecast air quality make it difficult to quantify the real level of air pollution in this area. The chemistry-transport model CHIMERE has been coupled with the meteorological model WRF and used to simulate hourly concentrations of Particulate Matter PM2.5 for three East African urban conurbations: Addis Ababa in Ethiopia, Nairobi in Kenya and Kampala in Uganda. Two existing emission inventories were combined to test the performance of CHIMERE as an air quality tool for a target monthly period of 2017 and the results compared against observed data from urban and rural sites. The results show that the model is able to reproduce hourly and daily temporal variability of aerosol concentrations close to observations both in urban and rural environments. CHIMERE’s performance as a tool for managing air quality was also assessed. The analysis demonstrated that despite the absence of high-resolution data and up-to-date biogenic and anthropogenic emissions, the model was able to reproduce 66–99 % of the daily PM2.5 exceedances above the WHO 24-hour mean PM2.5 guideline (25 µg m−3) in the three cities. An analysis of the 24-hour mean levels of PM2.5 was also carried out for 17 constituencies in the vicinity of Nairobi. This showed that 47 % of the constituencies in the area exhibited a low air quality index for PM2.5 in the unhealthy category for human health exposing between 10000 to 30000 people/km2 to harmful levels of air contamination.

Air pollution is one of the most important environmental and public health concerns worldwide. Urban air pollution has been increasing since the industrial revolution due to rapid industrialization, mushrooming of cities, and greater dependence on fossil fuels in urban centers. Particulate matter (PM) is considered to be one of the main aerosol pollutants that causes a significant adverse impact on human health. Low-cost air quality sensors have attracted attention recently to curb the lack of air quality data which is essential in assessing the health impacts of air pollutants and evaluating land use policies. This is mainly due to their lower cost in comparison to the conventional methods. The aim of this study was to assess the spatial extent and distribution of ambient airborne particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) in Nairobi City County. Seven sites were selected for monitoring based on the land use type: high- and low-density residential, industrial, agricultural, commercial, road transport, and forest reserve areas. Calibrated low-cost sensors and cyclone samplers were used to monitor PM2.5 concentration levels and gravimetric measurements for elemental composition of PM2.5, respectively. The sensor percentage accuracy for calibration ranged from 81.47% to 98.60%. The highest 24-hour average concentration of PM2.5 was observed in Viwandani, an industrial area (111.87 μg/m³), and the lowest concentration at Karura (21.25 μg/m³), a forested area. The results showed a daily variation in PM2.5 concentration levels with the peaks occurring in the morning and the evening due to variation in anthropogenic activities and the depth of the atmospheric boundary layer. Therefore, the study suggests that residents in different selected land use sites are exposed to varying levels of PM2.5 pollution on a regular basis, hence increasing the potential of causing long-term health effects.

This paper reports on the indoor and outdoor air quality in informal urban and rural settlements in Kenya. The study is motivated by the need to improve consciousness and to understand the harmful health effects of air quality to vulnerable people, especially in poor communities. Ng’ando urban informal settlement and Leshau Pondo rural village in Kenya are selected as representative poor neighborhoods where unclean energy sources are used indoor for cooking, lighting and heating. Filter based sampling for gravimetrical, elemental composition and black carbon (BC) analysis of particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5) is performed. findings from Ng’ando and Leshau Pondo showed levels exceeding the limit suggested by the world health organization (WHO), with rare exceptions. Significantly higher levels of PM2.5 and black carbon are observed in indoors than outdoor samples, with a differences in the orders of magnitudes and up to 1000 µg/m3 for PM2.5 in rural settlements. The elemental composition reveals the presence of potentially toxic elements, in addition to characterization, emission sources were also identified. Levels of Pb exceeding the WHO limit are found in the majority of samples collected in the urban locations near major roads with heavy traffic. Our results demonstrate that most of the households live in deplorable air quality conditions for more than 12 h a day and women and children are more affected. Air quality condition is much worse in rural settlements where wood and kerosene are the only available fuels for their energy needs.