Fine and coarse particulate matter (PM2.5, PM10) and black carbon (BC) pose significant health risks in rapidly urbanizing cities in low- and middle-income countries, particularly in sub-Saharan Africa. However, studies pertaining to their local impacts and disparities are less understood in these cities. This study modeled PM2.5, PM10, and BC differences in Accra, Ghana, to separate the impacts of local urban emissions from regional weather patterns and identify the specific drivers of pollution. Using reference-grade monitors, we conducted year-long, concurrent measurements of PM2.5, PM10, and BC at two sites with distinct land-use characteristics: a busy high-density urban core and a low-density urban backgroundThe results show significant urban pollution burden exceeding recommended guidelines for PM2.5 and PM10, with annual mean concentrations at the urban core site higher than the background site for PM2.5 (38.6 vs 27.9 mu g m(-3)), PM10 (99.3 vs 71.0 mu g m(-3)), and BC (7.5 vs 3.7 mu g m(-3)), underscoring the impact of local traffic and residential activities. A strong seasonal variation was also observed, with pollutant levels increasing significantly at both sites during the dry-Harmattan period (November-March) due to the transport of Saharan dust. Analysis of pollutant ratios (PM2.5/PM10 and BC/PM2.5) confirms a shift in aerosol type: coarse mineral dust dominates during the dry-Harmattan, while fine particles from local combustion sources are more prominent in the wet season. Using linear mixed effects models with PM10, the dry season, and daytime (06:00-17:59) as baseline variables, we demonstrated that PM10 pollution constitutes a persistent pollutant more than PM2.5 and BC at the high-density site, remaining significantly elevated (similar to 70% excess) regardless of the season. This study provides quantitative evidence of the dual impact of local land-use sources and regional weather on Accra's air quality. Our findings highlight the need for a two-track management approach that addresses both hyper-local emission sources and the large-scale impacts of seasonal phenomena like the Harmattan.

Sub-Saharan African (SSA) cities have high air and noise pollution levels, yet limited information exists on children's joint exposures at home and school locations - the two most important environments where children spend much of their time. This study characterised schoolchildren's exposure to multiple air pollutants and environmental noise and sound sources within the Accra School Health and Environment Study (ASHES). ASHES involved 1034 children aged 8-12 years from 90 public (73%) and private schools in Accra, Ghana, a major SSA city. Annual mean concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and black carbon (BC), as well as environmental noise (L day, L night, L den) and sound sources, were derived from land use regression models and linked to geocoded school and home locations for 919 children with valid data. A time-location weighted total exposure estimate was calculated for the air and noise pollution metrics. We also examined children's responses to a noise annoyance survey in relation to their noise exposure levels at home. For air pollutants, median home exposures were 33.8 μg m-3 (PM2.5), 55.5 μg m-3 (NO2), and 5.2 × 10-5m-1 (BC), and corresponding median school exposures were 29.9 μg m-3, 53.5 μg m-3, and 4.9 × 10-5m-1. The time location-weighted total air pollutant exposures for most children surpassed the respective World Health Organization (WHO) guideline. Similarly, median environmental noise levels (in dBA) at home (L den = 66.3 and L night = 54.2) and at school (L day = 62.4) exceeded WHO thresholds for road-traffic noise and Ghana's noise standard for areas with educational facilities, respectively. Pollution levels at home and school were moderately correlated (r [PM2.5] = 0.56; r [NO2] = 0.33; r [L den]= 0.47). On average, children attending public schools or living in lower income areas had higher exposures to both air and noise pollution than their private school (fee-paying) or higher-income counterparts. In contrast, nature-based sounds were more common in higher income areas. Among children exposed to L den noise levels between 65 and 70 dBA, 35% reported being highly annoyed to road traffic noise. Schoolchildren in Accra experience air and noise pollution levels exceeding health-based guidelines and standards. Exposures are unequally distributed, highlighting environmental inequalities with implications for child health, development, and learning.

Kigali, like many cities in sub-Saharan Africa (SSA), must balance rapid urban growth and the provision of essential services with the need to curb environmental pollution and protect public health in the context of its unique topography. Although the city has implemented policies aimed at reducing emissions from multiple sectors, systematic data on oxides of nitrogen (NO X ; NO 2 and NO), key markers of combustion-related urban air pollution, have been limited. We applied a standardized measurement protocol previously used in Accra, Ghana, to characterize city-scale spatial and temporal patterns of NO X pollution in Kigali. Between November 2022 and December 2023, we deployed Ogawa passive samplers to collect weekly integrated NO 2 (n = 630) and NO (n = 630) samples across 130 sites (10 year-long and 120 rotating week-long locations) representing diverse land-use types and source characteristics. Weekly NO 2 and NO concentrations ranged from approximately 2 to 62 µg/m3 (mean [SD]: 13.9 [11.4]) and approximately 1 to 49 µg/m³, respectively. Although nearly all background sites recorded NO 2 concentrations below the World Health Organization (WHO) annual guideline of 10 µg/m3, exceedances were common in more urbanized settings, occurring in 39% of samples from sparsely residential areas, 89% from commercial, business, and industrial (CBI) areas, and 99% from densely populated residential areas. Mean NO 2 concentrations were significantly higher in urban compared with rural neighborhoods (18.2 vs. 6.3 µg/m³; p < 0.001), at sites located within 200 m of primary roads compared with those farther away (19.9 vs. 11.6 µg/m3; p < 0.001), and at lower compared with higher elevations (15.4 vs. 9.0 µg/m3; p < 0.001). The levels were higher and exceeded the WHO annual guideline in the more densely populated and urbanized districts of Kicukiro and Nyarugenge, compared with the more rural Gasabo district. Similar spatial patterns were observed for NO. Overall, NO 2 and NO concentrations across Kigali were strongly patterned by land use, traffic proximity, population density, and topography, with the highest levels observed in traffic-dominated, densely populated, low-elevation areas. These city-wide measurement data provide critical evidence to inform land-use planning, air quality management, and regulatory strategies in a rapidly urbanizing, landlocked city characterized by complex topography.

Kigali, like many cities in sub-Saharan Africa, faces rapid urban growth alongside the need to manage air pollution and protect public health. Despite its policy efforts, systematic data on nitrogen oxides (NOx: NO2 and NO), key indicators of combustion-related pollution, have been limited. We applied a standardized protocol to characterize city-scale spatial and temporal patterns of NOx across Kigali. Between November 2022 and December 2023, we collected weekly integrated NO2 and NO samples (n = 630 each) at 130 sites representing diverse land-use types. NO2 concentrations ranged from 1.3 to 61.9 µg/m3 (mean 13.9 µg/m3), with annual-equivalent frequently exceeding the WHO annual guideline (10 µg/m3) in urban areas. Exceedances occurred in 39% of sparsely residential, 89% of commercial/industrial, and 99% of densely populated residential sites. NO2 concentrations were significantly higher in urban versus rural areas (18.2 vs. 6.3 µg/m3), near major roads (19.9 vs. 11.6 µg/m3), and at lower elevations (15.4 vs. 9.0 µg/m3). The highest levels were observed in the densely populated districts of Kicukiro and Nyarugenge. Overall, NO2 and NO exhibited strong spatial gradients related to land use, traffic, population density, and topography, highlighting the importance of targeted urban planning and air quality management in rapidly growing cities.

Cities have complex dynamics on timescales from hourly and daily changes to monthly shifts and annual trends. We used time-lapsed street-view imagery (SVI) to capture and analyse temporal trends of urban environmental features in Accra, Ghana. We collected a novel dataset of 6.8 million street-view images (SVI) at five-minute intervals over five years at ten representative locations in Accra, Ghana. We used a fine-tuned YOLOv7 object detection model to detect and obtain counts of people, large vehicles, small vehicles, two-wheelers, market-related objects, refuse and animals in all images. We used a mixed-effects zero-inflated negative binomial model with indicators for hour of day, day of week, week of year, and year to consistently and coherently identify temporal patterns of object counts across time scales and sites. People and small vehicles were most prevalent in mid-morning and early evening, and market-related objects peaked in early afternoon. The number of people, vehicles and market-related objects declined on weekends at most sites, although two residential sites showed an inverse trend, peaking in all three categories on weekends. Long-term trends over the years indicate a rise in people at high-density residential sites. Over the same period, market-related objects, two-wheelers and small vehicles declined at several locations with different land-use characteristics, suggesting broad shifts in transport and commercial activity. These results demonstrate the potential of SVI and computer vision for urban monitoring to support strategies for improving mobility, traffic congestion, pollution, access to goods and services, and waste management.

Abstract Ambient air pollution has been linked to elevated blood pressure (BP) in adults, but research is limited among children, particularly in sub-Saharan Africa (SSA). We investigated the potential effects of ambient fine particulate matter (PM 2.5 ), black carbon (BC), and nitrogen dioxide (NO 2 ) exposures on BP in school-aged children in Accra, Ghana’s capital and one of the fastest growing cities in SSA. We performed a cross-sectional analysis among 919 (60% girls) schoolchildren aged 7–14 years across 90 elementary schools. Following NIH guideline, we define elevated BP as age, sex, and height specific systolic and/or diastolic BP values at ≥ 90th percentile. PM 2.5 , BC, and NO 2 concentrations at homes and schools were estimated using spatiotemporal land-use regression models developed specifically for Accra using measurement data from 146 sites. Covariate-adjusted associations were estimated using multivariable mixed-effects linear and logistic regression models. The mean SBP and DBP among the children were 107.2 (9.7) and 69.1 (7.1) mmHg, respectively, with one-third of the children having elevated BP. Children’s PM 2.5 exposure at both home and school were 5–8 times the World Health Organization (WHO) annual guideline of 5 µg/m 3 , BC levels were above the maximum value in the “good practice statement for BC” (5.1 µg/m 3 ), and NO 2 concentrations exceeded the WHO annual guideline of 10 µg/m 3 by 11-fold at some locations. Overall, PM 2.5 , BC, and NO 2 were negatively associated with BP after adjustment for demographic and lifestyle covariates. For instance, a 10 unit increase in air pollution at homes was associated with a small decrease in SBP (PM 2.5 : −0.70 mmHg (95% CI −3.34, 1.94), BC: −4.61 mmHg (95% CI −9.59, 0.37), NO 2 : -0.19 mmHg (95% CI −0.60, 0.22) and DBP (PM 2.5 : −0.83 mmHg (95% CI −2.93, 1.28), BC: −4.35 mmHg (95% CI −8.19, −0.50), NO 2 : −0.14 mmHg (95% CI −0.46, 0.18)). Furthermore, home-level PM 2.5 was associated with reduced odds of having elevated BP (OR 0.75 (95% CI 0.39, 1.41)), as was BC (OR 0.35 (95% CI 0.09, 1.39)). This study showed no evidence of pro-hypertensive effect of exposure to PM 2.5 , BC, and NO 2 pollution among schoolchildren in Accra.

Elementary school and early education are crucial for children’s cognitive and social development, as well as lifetime health and well-being. For children in cities, urban schools present numerous advantages in education quality and access to resources and opportunities that stimulate learning and improve health. In Sub-Saharan African (SSA) cities, the complexity of the urban environment requires careful consideration of school environments in enhancing child health and development. Yet, little is known about environmental conditions in schools and schoolchildren’s health in rapidly urbanizing SSA cities. This paper describes the various datasets captured within the Accra School Health and Environment Study (ASHES), a study platform designed to characterize air and noise pollution at elementary schools and for schoolchildren, and their influence on key markers of childhood health and development. We outline environmental exposures and health and developmental outcomes among children living in a major metropolitan area in SSA, along with preliminary results and planned analyses. ASHES was implemented in Accra, one of the fastest growing metropolises in SSA. Between July 2022 and May 2023, 1,037 children (∼60% female) aged 8-12 were recruited from 90 public (74%) and private primary schools. Weeklong fine particulate matter (PM 2.5 ), black carbon (BC), and sound pressure levels were measured in the schoolyards. Homes of the children were geocoded and linked with spatial prediction models to estimate ambient pollutant concentrations at each child’s residence. Data were also captured on anthropometry, blood pressure, respiratory function, cognitive and behavioral functions, and sleep quality. Questionnaires gathered additional information on school, household, and sociodemographic factors. Preliminary results suggest that a third of children were hypertensive, 30% were overweight or obese, and 14% had behavioral problems. PM 2.5 and noise levels across schools exceeded local and international standards. Several ongoing epidemiologic analyses will examine the key exposures in relation to the major outcomes.

Air and noise pollution are significant emerging environmental health hazards in African cities, with potentially complex spatial and temporal patterns. Limited local data are a major barrier to the formulation and evaluation of policies to reduce air and noise pollution.We designed and carried out an innovative 3-year measurement campaign to characterise air and noise pollution and their sources at high-resolution within the Greater Accra Metropolitan Area (GAMA), Ghana. Our design used a combination of fixed (3 year-long, n=10) and rotating (week-long over 1 year, n =136) sites, selected to represent a range of land uses and source influences. We collected data on PM2.5, black carbon (BC), nitrogen oxides (NOx), weather variables, noise pollution, along with street level time-lapse images with cameras. To do this, we strategically deployed low-cost, low-power, lightweight monitoring devices in an integrated station that was robust, socially unobtrusive, and able to function in the West African coastal climate. We used spatiotemporal land use regression models to predict PM2.5, NO2, BC and noise pollution across the city in high spatial resolution, and state-of-the-art methods in deep learning to predict pollution levels in high temporal resolution by training classification algorithms on 2 million time-lapse images captured at street level with corresponding pollution measurements. Most measurement sites recorded air pollution and noise levels above the WHO health-based guidelines. Spatiotemporal LUR models achieved good out of sample R2’s of 0.51-0.54 (noise), 0.58 – 0.83 (PM2.5), 0.78 - 0.80 (NO2) and 0.79 – 0.88 (BC). From the deep learning image-based analysis, the classification (prediction) accuracy of noise levels in space and time was higher (40-70%) than PM2.5 (30-55%), due to the localised nature of noise source emissions, and the fine-grained nature of our classes, which distinguish between small changes than previous studies. Our approach to monitoring and modelling air and noise pollution can be scaled up in other SSA cities to fill critical data gaps, and is already being successfully piloted in Kigali, Rwanda. The exposure surfaces developed with the LUR models are now supporting ongoing epidemiological studies assessing the impact of exposure to air and noise on birth outcomes and child health and development in Accra. Street view imagery are an increasingly available resource in cities around the world (from CCTV; Google Street View), and results from our deep learning image-based analysis show that the time lapsed images are a uniquely informative source of data for predicting high resolution temporal change in exposure, simultaneously with the presence or absence of potential determinants, though integrating with high spatial resolution remains a challenge.

Principles of dense, mixed-use environments and pedestrianisation are influential in urban planning practice worldwide. A key outcome espoused by these principles is generating "urban vitality", the continuous use of street sidewalk infrastructure throughout the day, to promote safety, economic viability and attractiveness of city neighbourhoods. Vitality is hypothesised to arise from a nearby mixture of primary uses, short blocks, density of buildings and population and a diversity in the age and condition of surrounding buildings. To investigate this claim, we use a novel dataset of 2.1 million time-lapsed day and night images at 145 representative locations throughout the city of Accra, Ghana. We developed a measure of urban vitality for each location based on the coefficient of variation in pedestrian volume over time in our images, obtained from counts of people identified using object detection. We also construct measures of "generators of diversity": mixed-use intensity, building, block and population density, as well as diversity in the age of buildings, using data that are available across multiple cities and perform bivariate and multivariate regressions of our urban vitality measure against variables representing generators of diversity to test the latter's association with vitality. We find that two or more unique kinds of amenities accessible within a five-minute walk from a given location, as well as the density of buildings (of varying ages and conditions) and short blocks, are associated with more even footfall throughout the day. Our analysis also indicates some potential negative trade-offs from dense and mixed-use neighbourhoods, such as being associated with more continuous road traffic throughout the day. Our methodological approach is scalable and adaptable to different modes of image data capture and can be widely adopted in other cities worldwide.

Cities in sub-Saharan Africa (SSA) are undergoing significant economic and urban expansion. The rapid urban growth is shaping land use, housing, transportation, and energy for household and commercial use. Consequently, air pollution from diverse local and regional sources and with complex space-time patterns has emerged as a major environmental health concern for cities in SSA. Yet, limited city-scale data are a barrier to climate and health impact assessment as well as policy formulation and evaluation to reduce air pollution. We are implementing and testing the transferability of Pathways to Equitable Health Cities measurement protocol for Accra in Kigali, Rwanda. The protocol is designed to generate rich environmental pollution data in SSA cities. Both Accra and Kigali are representative of the rapid urbanization and economic transformation that are happening across SSA. We have assembled and integrated multiple low-cost, low-power, lightweight sensors that have been validated in prior studies to measure integrated and real-time fine particulate matter (PM2.5), black carbon (BC), and oxides of nitrogen (NO2 and NO) concentrations at city-scale. Initiated in November 2022, our year-long measurement campaign utilizes a network and combination of ‘fixed’ (n=10) and ‘rotating’ (n =120) monitoring sites. The sites represent variety of land uses and emission sources, including background, road traffic, commercial, industrial and residential areas, and neighbourhood socioeconomic classes. The fixed sites are monitored continuously for one year to capture temporal (annual and seasonal) patterns, whereas the rotating sites are monitored for one week (in groups of four per week) to capture spatial variations in the pollutant concentrations. In addition to the air pollutants, we are also collecting data on environmental noise and weather variables (i.e. temperature, relative humidity and wind speed/direction) to aid in the analyses. The Kigali initiative is being implemented in partnership with AIMS-Rwanda and the Rwanda Environment Management Authority (REMA) to promote capacity building within the government. Planned analyses involve the use state-of-the-art models, including spatial statistics, deep/machine learning approaches, to capture highly resolved temporal and spatial variations as well as socioeconomic inequalities in pollution levels across Kigali city and to identify sources and their relative contributions. The data form the basis of future climate change and air pollution forecasting and health impact assessment as well as policy evaluation and emission reduction scenarios in the city.

Cities encompass a mixture of artificial, human, animal, and nature-based sounds, which through long and short-term exposures, can impact on physical and mental health. Yet, most epidemiological research has focused on only transportation noise, leaving a significant gap in understanding the health impacts of other urban sound types, especially in sub-Saharan Africa (SSA). We conducted a large-scale measurement campaign in Accra, Ghana, collecting audio recordings and sound levels from 129 locations between April 2019-June 2020. We classified sound types with a neural network model and then used Random Forest land use regression to predict prevalences of different sound types citywide. We then developed a composite metric integrating sound levels with the prevalence of sound types. Road traffic sounds dominated the urban core, while human and animal sounds were prominent in high-density and peri-urban areas, respectively. Our high-resolution approach provides a comprehensive characterization of the complexity of urban sounds in a major SSA city, paving the way for new epidemiological studies on the health impacts of exposure to diverse sound sources in the future.

In Sub-Saharan African (SSA) cities, elementary school environments may significantly contribute to children’s exposure to environmental pollution, potentially affecting their health, development, and learning. Despite children spending much of their day at school, limited data exists regarding levels, inequalities, and determinants of air and noise pollution in school settings, particularly in rapidly urbanizing regions. As part of the Accra School Health and Environment Study (ASHES), we assessed air and noise pollution in primary schools across the Greater Accra Metropolitan Area, one of SSA’s fastest-growing metropolises, and explored determinants of pollution levels around these schools. We conducted weeklong measurements of fine particulate matter (PM _2.5 ), black carbon (BC), and sound pressure levels in 90 schoolyards (74% public, 26% private). We assessed schoolyard characteristics (surface type, greenness, road proximity) and examined their associations with pollutants using generalized additive models. Additionally, we evaluated 1037 child responses to noise annoyance surveys. Annual equivalent PM _2.5 concentrations exceeded WHO guidelines by 2–13 times (11–65 µ g m ^−3 ). Median noise levels (57 dBA) surpassed Ghana EPA standards at >60% of schools, coinciding with 60% of students reporting high noise annoyance. BC and noise were higher in public and more urban schools. In the most urbanized district, all pollutants were inversely associated with neighborhood socioeconomic status. Lower greenness correlated with higher BC levels; associations with other spatial factors were weak or not statistically significant. These findings underscore the need to reduce air and noise pollution at urban SSA schools and promote healthier, quieter environments that support learning and development.

As cities in sub-Saharan Africa become more crowded, noise pollution is also emerging as an important environmental concern, after air pollution. Yet, unlike air pollution, which is enjoying relatively more public attention, there is limited measurement data and policy efforts on environmental noise pollution. We followed a recent city-wide measurement approach used in Accra (Ghana) and characterized environmental noise patterns in Kigali, a contrasting city with very different topography and regulatory system than Accra to inform urban policy. We established 10 ‘fixed’ (yearlong) and 120 ‘rotating’ (weeklong) monitoring sites to capture both the temporal and spatial patterns in Kigali’s sound environment. The measurement occurred between November 2022 and December 2023, and samples were collected at 1 min interval, resulting in 5155 014 (3580 site-days) and 1190 620 (827 site-days) site-minutes of valid data from the fixed and rotating sites, respectively. The 130 monitoring sites covered a variety of geographic and land-use factors across diverse neighborhoods and sources. We computed several noise metrics, including 1 h (LAeq _1 h ), daily (LAeq _24 h ), day-time ( L _day ), and night-time ( L _night ). Daily noise (LAeq _24 h ) levels across the city ranged between 38 dBA and 85 dBA. Commercial, business, and industrial (CBI) and high-density residential (HD) communities experienced the highest noise levels, with some sites constantly above 70 dBA at day and 65 dBA at night. About 63% of our observed day-time values (up to ∼72% in some areas) exceeded the Rwandan day-time standard (55 dBA) for residential areas, whereas 69% of the observed night-time values (up to 80% in some areas) exceeded the corresponding night-time standard (45 dBA). In Nyarugenge, the most urbanized district, as much as 75% of our site-days data exceeded day-time standard. However diurnal patterns throughout the city were similar, rising from ∼5 am, peaking at about 8 am and plateauing until 6 pm before falling to their lowest at midnight. Overall, noise levels in the city did not vary much by day of the week, weekdays vs weekend, or dry vs wet seasons. Environmental noise in Kigali often exceeded both Rwandan standards and international guidelines, with residents in the city center district, CBI and HD areas at risk of higher exposure, and hence higher risk of adverse effects. Detailed assessment of the sources, at-risk population, and associated health effects may inform Rwandan’s environmental policy efforts and city initiatives in the face of the ongoing urban growth and densification.

Fine particulate matter (PM2.5) pollution represents a major environmental health risk in Africa. The use of low-cost sensors (LCS) for air quality monitoring for policy and civic engagement in sub-Saharan Africa (SSA) has become paramount, as access to traditional reference-grade instruments is still sparse. Yet, studies pertaining to sensor performance under SSA's meteorological conditions and diverse emission sources are limited. Hence, we tested eight low-cost PM2.5 sensors on the market from different manufacturers containing Plantower PMS, Alphasense OPC-N3, and AVO-Sensor sensors by collocating them with the federal equivalent method Teledyne T640 to ascertain data accuracy, reliability, and responsiveness during wet and dry periods. After 6 months of collocation, PM2.5 concentrations from the LCS showed low intrasensor variability in both the wet and dry periods, but high intersensor variability with the Teledyne T640. A strong relationship existed between the LCS and Teledyne T640, with average coefficient of determination (R2) values of 0.7 (range: 05-0.9) and 0.8 (0.64-0.97) in the wet and dry periods, respectively. Larger errors were also associated with LCS data during the dry than the wet period, with the average mean absolute error and root mean squared error, respectively, 4.5 and 5.3 times higher in the dry period. Uncertainties with large errors were also observed with high PM2.5 measured in the wet period, levels that were more common during the dry period typically characterized by long-range transport of PM2.5 pollution. The results show that season significantly affects LCS performance and data quality and that care must be taken during deployment and data usage in SSA, with regular maintenance, particularly in the dry season. Strong collaborative efforts between governmental agencies, industries, and civil society are needed to come up with an effective framework for their application.

Urban air pollution is a critical public health challenge in low-and-middle-income countries (LMICs). At the same time, LMICs tend to be data-poor, lacking adequate infrastructure to monitor air quality (AQ). As LMICs undergo rapid urbanization, the socio-economic burden of poor AQ will be immense. Here we present a globally scalable two-step deep learning (DL) based approach for AQ estimation in LMIC cities that mitigates the need for extensive AQ infrastructure on the ground. We train a DL model that can map satellite imagery to AQ in high-income countries (HICs) with sufficient ground data, and then adapt the model to learn meaningful AQ estimates in LMIC cities using transfer learning. The trained model can explain up to 54% of the variation in the AQ distribution of the target LMIC city without the need for target labels. The approach is demonstrated for Accra in Ghana, Africa, with AQ patterns learned and adapted from two HIC cities, specifically Los Angeles and New York.

Introduction: Despite the broad improvement in air quality, air pollution remains a major leading global risk factor for ill health and deaths each year. Air pollution has a significant impact on both health and economic growth in Africa. This paper reviews the health impacts of air pollution and the benefits of air pollution mitigation and prevention on climate change. Methods: We conducted a narrative review and synthesized current literature on the health impact of air pollution in the context of changing climate in Africa. Results: Particulate matter (PM2.5) concentrations in Africa pose significant health risks due to various sources, including household fuels and industrial emissions. Limited air quality monitoring hampers accurate assessment and public health planning. Africa's rapid urbanization exacerbates air pollution, impacting vulnerable populations disproportionately. Renewable energy adoption and improved monitoring infrastructure are crucial for mitigating air pollution's economic and health impacts. Recommendations include adopting air quality standards, identifying pollution sources, and prioritizing interventions for vulnerable groups. Integrating renewable energy into development plans is essential for sustainable growth. African leaders must prioritize environmental policies to safeguard public health amid ongoing industrialization. Conclusions: Air pollution prevention remains a vital concern that requires leaders to engage stakeholders, and other opinion leaders in society. African leaders should proactively explore new avenues to integrate non‑polluting renewable energy sources such as solar power, wind and hydropower into their national development plans.

Road traffic has become the leading source of air pollution in fast-growing sub-Saharan African cities. Yet, there is a dearth of robust city-wide data for understanding space-time variations and inequalities in combustion related emissions and exposures. We combined nitrogen dioxide (NO2) and nitric oxide (NO) measurement data from 134 locations in the Greater Accra Metropolitan Area (GAMA), with geographical, meteorological, and population factors in spatio-temporal mixed effects models to predict NO2 and NO concentrations at fine spatial (50 m) and temporal (weekly) resolution over the entire GAMA. Model performance was evaluated with 10-fold cross-validation (CV), and predictions were summarized as annual and seasonal (dusty [Harmattan] and rainy [non-Harmattan]) mean concentrations. The predictions were used to examine population distributions of, and socioeconomic inequalities in, exposure at the census enumeration area (EA) level. The models explained 88% and 79% of the spatiotemporal variability in NO2 and NO concentrations, respectively. The mean predicted annual, non-Harmattan and Harmattan NO2 levels were 37 (range: 1-189), 28 (range: 1-170) and 50 (range: 1-195) mu g m(-3), respectively. Unlike NO2, NO concentrations were highest in the non-Harmattan season (41 [range: 31-521] mu g m(-3)). Road traffic was the dominant factor for both pollutants, but NO2 had higher spatial heterogeneity than NO. For both pollutants, the levels were substantially higher in the city core, where the entire population (100%) was exposed to annual NO2 levels exceeding the World Health Organization (WHO) guideline of 10 mu g m(-3). Significant disparities in NO2 concentrations existed across socioeconomic gradients, with residents in the poorest communities exposed to levels about 15 mu g m(-3) higher compared with the wealthiest (p < 0.001). The results showed the important role of road traffic emissions in air pollution concentrations in the GAMA, which has major implications for the health of the city's poorest residents. These data could support climate and health impact assessments as well as policy evaluations in the city.