Inequalities in urban air pollution in sub-Saharan Africa: an empirical modeling of ambient NO and NO2 concentrations in Accra, Ghana.

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) µg m-3, respectively. Unlike NO2, NO concentrations were highest in the non-Harmattan season (41 [range: 31-521] µg 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 µg m-3. Significant disparities in NO2 concentrations existed across socioeconomic gradients, with residents in the poorest communities exposed to levels about 15 µg 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.

A gist on an obscure neoplasm in Ghana: gastrointestinal stromal tumours.

BACKGROUND: Gastrointestinal Stromal Tumour is a rare but potentially curable tumour of the gastrointestinal tract accounting for up to 1% of all gastrointestinal tumours. The discovery of Imatinib mesylate, a novel tyrosine kinase inhibitor has improved the chances even for unresectable, recurrent, or metastatic diseases. METHODS: This study sought to document the clinical and pathological characteristics of GISTs from two tertiary hospitals in Ghana that have undergone immunohistochemistry confirmation between 2014 and 2021. RESULTS: The median age of the subjects was 50 years with most of them (28.0%) being above 61 years. There were more females than males (64.0% vs. 36.0%). Abdominal mass and abdominal pain made up the majority of the clinical presentations. The majority of the subjects had partial gastrectomy (32.0%) which was followed by wedge resection (28.0%). Appendectomy and sleeve gastrectomy were the least performed procedures (8% each). Four of the 25 patients (16.0%) had resections of involved contiguous organs done with splenectomy being the most common procedure. The majority of GISTs were found in the stomach (68.0%) followed by the appendix (12.0%) and small bowel (12.0%). Gastrointestinal bleeding (55.8%) and abdominal pain (38.5%) were the most reported symptoms. Free resection margins were observed in 84.0% of the subjects and only 3/25 (12.0%) experienced tumour recurrence. CONCLUSION: GIST is a potentially curable tumour that once was obscure but currently gaining popularity. Surgical resection offers the hope of a cure for localized disease while targeted therapies is a viable option for recurrent, metastatic, or unresectable tumours.

Identifying deprived "slum" neighbourhoods in the Greater Accra Metropolitan Area of Ghana using census and remote sensing data.

Background: Identifying urban deprived areas, including slums, can facilitate more targeted planning and development policies in cities to reduce socio-economic and health inequities, but methods to identify them are often ad-hoc, resource intensive, and cannot keep pace with rapidly urbanizing communities. Objectives: We apply a spatial modelling approach to identify census enumeration areas (EAs) in the Greater Accra Metropolitan Area (GAMA) of Ghana with a high probability of being a deprived area using publicly available census and remote sensing data. Methods: We obtained United Nations (UN) supported field mapping data that identified deprived "slum" areas in Accra's urban core, data on housing and population conditions from the most recent census, and remotely sensed data on environmental conditions in the GAMA. We first fitted a Bayesian logistic regression model on the data in Accra's urban core (n=2,414 EAs) that estimated the relationship between housing, population, and environmental predictors and being a deprived area according to the UN's deprived area assessment. Using these relationships, we predicted the probability of being a deprived area for each of the 4,615 urban EAs in GAMA. Results: 899 (19%) of the 4,615 urban EAs in GAMA, with an estimated 745,714 residents (22% of its urban population), had a high predicted probability (≥80%) of being a deprived area. These deprived EAs were dispersed across GAMA and relatively heterogeneous in their housing and environmental conditions, but shared some common features including a higher population density, lower elevation and vegetation abundance, and less access to indoor piped water and sanitation. Conclusion: Our approach using ubiquitously available administrative and satellite data can be used to identify deprived neighbourhoods where interventions are warranted to improve living conditions, and track progress in achieving the Sustainable Development Goals aiming to reduce the population living in unsafe or vulnerable human settlements.

Beyond here and now: Evaluating pollution estimation across space and time from street view images with deep learning.

Advances in computer vision, driven by deep learning, allows for the inference of environmental pollution and its potential sources from images. The spatial and temporal generalisability of image-based pollution models is crucial in their real-world application, but is currently understudied, particularly in low-income countries where infrastructure for measuring the complex patterns of pollution is limited and modelling may therefore provide the most utility. We employed convolutional neural networks (CNNs) for two complementary classification models, in both an end-to-end approach and as an interpretable feature extractor (object detection), to estimate spatially and temporally resolved fine particulate matter (PM2.5) and noise levels in Accra, Ghana. Data used for training the models were from a unique dataset of over 1.6 million images collected over 15 months at 145 representative locations across the city, paired with air and noise measurements. Both end-to-end CNN and object-based approaches surpassed null model benchmarks for predicting PM2.5 and noise at single locations, but performance deteriorated when applied to other locations. Model accuracy diminished when tested on images from locations unseen during training, but improved by sampling a greater number of locations during model training, even if the total quantity of data was reduced. The end-to-end models used characteristics of images associated with atmospheric visibility for predicting PM2.5, and specific objects such as vehicles and people for noise. The results demonstrate the potential and challenges of image-based, spatiotemporal air pollution and noise estimation, and that robust, environmental modelling with images requires integration with traditional sensor networks.

The effects of dietary microbial 6-phytase on growth parameters, intestinal morphometric properties and selected intestinal genes expression in rainbow trout (Oncorhynchus mykiss, Walbaum 1876).

This study investigated the effects of dietary 6-phytase, produced by a genetically modified Komagataella phaffii, on growth performance, feed utilisation, flesh quality, villus morphometric properties, and intestinal mRNA expression in rainbow trout. Six iso-nitrogenous, iso-lipidic, and iso-caloric diets were formulated and fed to triplicate groups of juvenile rainbow trout weighing 32.57 ± 0.36 g (mean ± SD) for 90 days. The dietary treatments included two positive controls (PC), one formulated with 400 g/kg of fish meal named T1, and the other formulated with 170 g/kg of fish meal plus 1% avP derived from monocalcium phosphate named T2. The remaining dietary treatments consisted of a negative control (NC) formulated with 170 g/kg of fish meal (T3), NC+ 750, NC+ 1500, and NC+ 3000 OTU/kg levels of phytase designated as T4, T5, and T6 diets respectively. Compared to T1, weight gain (WG) increased by 16.29, 13.71 and 11.66% in T4, T5 and T6, respectively (p < 0.05). Feed conversion ratio (FCR) was lowered by 3.2 and 0.8% in T4 and T5 compared to T1 (p < 0.05). WG, feed intake (FI), FCR, final body length, bone ash, bone ash P, and intestinal morphometry were negatively affected in T3 fed fish (p < 0.05). Whole-body fish nutrient, bone ash, bone ash phosphorus (P) compositions and mucosal villus morphometric properties improved in rainbow trout fed diets supplemented with phytase dose ranging from 750-3000 OTU. Bone ash increased by 6.12% in T5 compared to T1 (p < 0.05). Phytase inclusion enhanced the profitability of feeding juvenile rainbow trout such diets as it reduced the feed price and economic conversion rate. Dietary inclusion of phytase down-regulated mRNA expression of genes responsible for fatty acid synthesis and lipogenesis in juvenile rainbow trout. Dietary phytase up-regulated the mRNA expression of genes (SLC4A11 and ATP1A3A) responsible for nutrient uptake and down-regulated intestinal expression of MUCIN 5AC-like genes (mucus secreting genes) in juvenile rainbow trout. Along with improving performance parameters, the inclusion of phytase in rainbow trout diet containing plant-based protein sources, can preserve intestinal morphology by regulating the mRNA expression of genes responsible for fatty acid synthesis, lipogenesis and nutrient uptake and transport.

Phenotyping urban built and natural environments with high-resolution satellite images and unsupervised deep learning.

Cities in the developing world are expanding rapidly, and undergoing changes to their roads, buildings, vegetation, and other land use characteristics. Timely data are needed to ensure that urban change enhances health, wellbeing and sustainability. We present and evaluate a novel unsupervised deep clustering method to classify and characterise the complex and multidimensional built and natural environments of cities into interpretable clusters using high-resolution satellite images. We applied our approach to a high-resolution (0.3 m/pixel) satellite image of Accra, Ghana, one of the fastest growing cities in sub-Saharan Africa, and contextualised the results with demographic and environmental data that were not used for clustering. We show that clusters obtained solely from images capture distinct interpretable phenotypes of the urban natural (vegetation and water) and built (building count, size, density, and orientation; length and arrangement of roads) environment, and population, either as a unique defining characteristic (e.g., bodies of water or dense vegetation) or in combination (e.g., buildings surrounded by vegetation or sparsely populated areas intermixed with roads). Clusters that were based on a single defining characteristic were robust to the spatial scale of analysis and the choice of cluster number, whereas those based on a combination of characteristics changed based on scale and number of clusters. The results demonstrate that satellite data and unsupervised deep learning provide a cost-effective, interpretable and scalable approach for real-time tracking of sustainable urban development, especially where traditional environmental and demographic data are limited and infrequent.

Socio-demographic and neighbourhood factors influencing urban green space use and development at home: A population-based survey in Accra, Ghana.

In Sub-Saharan Africa and other developing regions, there has been very little systematic attempt to document the uses and perceived health benefits of urban green spaces in cities and the factors influencing usage. We therefore sought to establish the availability, accessibility and use of urban green spaces, and the perceived health benefits in an African population. We also ascertained the factors influencing use and development of green spaces at home. A population-based survey was conducted in Accra, the capital city of Ghana, spanning 11 Municipal and 3 Sub-Metropolitan areas. Multivariable binary logistic regression adjusting for potential confounders was used to establish the association between green space use and development at home, and socio-demographic, neighbourhood and health factors. Odds ratios and their corresponding 95% confidence intervals were estimated from the models. Several socio-demographic (gender, age, marital status, occupation, ethnicity, religion) and district-level (population density, income level, neighbourhood greenness) factors were associated with use of green spaces and development of green spaces at home in Accra. Residents who were worried about depletion of green spaces in their community were more likely to develop green spaces at home. In neighbourhoods with moderate and high level of greenness, residents were less likely to develop green spaces at home. Five-percent and 47% of green space users in Accra reported witnessing an improvement in their physical and mental health, respectively, from use of green spaces. The study findings can inform policy action for promoting use and development of green spaces in African cities and for mitigating depletion and degradation of the limited urban greenery.

Spatial distribution of Hg in Pra River Basin, Southwestern Ghana using HF acid combination method.

The study assessed the spatial distribution of total mercury (THg) in soils, sediments, mining wastes, and Au-rich Hg-contaminated tailings from artisanal and small-scale gold mining (ASGM) from Offin, Lower and Upper Pra, Birim, and Anum Rivers, Pra River Basin, Southwestern Ghana. THg measurement using Cold Vapor Atomic Absorption Spectrometry (CVAAS) after acid digestion with HNO3/HCl/HF and k0-INAA, as a reference method, and both provided comparable results. A digestion method, HNO3/HClO2/H2SO4 acid mixture before CVAAS provided lower results, which indicates that the use of HF is of fundamental importance in THg analysis based on acid digestion and its omission may significantly underestimate the presence of Hg in soils and sediments. THg in soils, sediments, Au-rich Hg-contaminated tailings, and mining wastes from the river basin were liberated into a solution for measurement using HNO3/HCl/HF. The study revealed Offin and Lower Pra Rivers showed high distribution (ranges; mg Hg kg-1) of THg in soils (103-770) and sediments (0.20-20.8), respectively; Upper Pra and Anum rivers showed the lowest THg in soils (2.20-3.20) and sediments (0.004-0.02), respectively. About 76.0% of THg in sediments was lower than the USEPA guideline of 0.2 mg Hg kg-1. The highest mean THg (mg Hg kg-1) in Au-rich Hg-contaminated tailings (1673 ± 4.8, n = 4) and mining wastes (17.3-21.5) were from the river Offin. The study showed Offin (Dunkwa-on-Offin site 1) and Lower Pra (Beposo Township) rivers are Hg hotspots that need attention.

Socio-demographic effects on role assignment and associated occupational health and safety issues in artisanal and small-scale gold mining in Amansie Central District, Ghana.

The study employed the binary logistic regression model, Chi-square, and likelihood ratio test to explore the nexus between socio-demographic characteristics and role assignment as well as Occupational Health and Safety (OHS) issues in artisanal and small-scale gold mining (ASGM) undertakings in the Amansie Central District of Ghana. Simple random sampling was employed to sample 250 respondents from three (3) different mining sites. The results revealed that socio-demographic variables such as age, gender, and work experience significantly impacted the type of role assumed by individuals in ASGM undertakings. For the socio-demographic effects on OHS issues, male respondents especially those in the age group between 18 and 35 with less work experience and education had a higher risk of recording injuries/accidents. Other risk factors such as types of role, reasons for ASGM, awareness of OHS hazards, awareness of Personal Protective Equipment (PPE), usage of PPE, arrest for not using PPE, cost of PPE, and frequency of buying PPE had a statistically significant influence on the occurrence of injuries/accidents. It is recommended that the Government implement initiatives to ensure that workers in ASGM operations in Ghana are provided with training, education, resources, and support services to ensure their safety and well-being, taking into account their socio-demographic characteristics. Also, the government and related stakeholders create more jobs through sustainable mining with long-term potential in local districts to address goals 1 (No poverty) and 2 (zero hunger) of the sustainable development goals.

High-resolution patterns and inequalities in ambient fine particle mass (PM2.5) and black carbon (BC) in the Greater Accra Metropolis, Ghana.

Growing cities in sub-Saharan Africa (SSA) experience high levels of ambient air pollution. However, sparse long-term city-wide air pollution exposure data limits policy mitigation efforts and assessment of the health and climate effects. In the first study of its kind in West Africa, we developed high resolution spatiotemporal land use regression (LUR) models to map fine particulate matter (PM2.5) and black carbon (BC) concentrations in the Greater Accra Metropolitan Area (GAMA), one of the fastest sprawling metropolises in SSA. We conducted a one-year measurement campaign covering 146 sites and combined these data with geospatial and meteorological predictors to develop separate Harmattan and non-Harmattan season PM2.5 and BC models at 100 m resolution. The final models were selected with a forward stepwise procedure and performance was evaluated with 10-fold cross-validation. Model predictions were overlayed with the most recent census data to estimate the population distribution of exposure and socioeconomic inequalities in exposure at the census enumeration area level. The fixed effects components of the models explained 48-69 % and 63-71 % of the variance in PM2.5 and BC concentrations, respectively. Spatial variables related to road traffic and vegetation explained the most variability in the non-Harmattan models, while temporal variables were dominant in the Harmattan models. The entire GAMA population is exposed to PM2.5 levels above the World Health Organization guideline, including even the Interim Target 3 (15 µg/m3), with the highest exposures in poorer neighborhoods. The models can be used to support air pollution mitigation policies, health, and climate impact assessments. The measurement and modelling approach used in this study can be adapted to other African cities to bridge the air pollution data gap in the region.

C-kit-negative transmural gastrointestinal stromal tumor in the stomach: A rare case of upper GI bleeding in Ghana.

Upper GI bleeding from GIST is rare in Africa. The C-kit-negative variant is uncommon worldwide. We report a case of a 42-year-old woman managed for upper GI bleeding secondary to stomach GIST, negative for CD117 but positive for DOG 1 and CD 34. The 1-year postsurgery review is uneventful.

Clinicopathologic characteristics of early-onset breast cancer: a comparative analysis of cases from across Ghana.

BACKGROUND: Breast cancer is the commonest cancer diagnosed globally and the second leading cause of cancer-related mortality among women younger than 40 years. This study comparatively reviewed the demographic, pathologic and molecular features of Early-Onset Breast Cancer (EOBC) reported in Ghana in relation to Late Onset Breast Cancer (LOBC). METHODS: A descriptive, cross-sectional design was used, with purposive sampling of retrospective histopathology data from 2019 to 2021. Reports of core or incision biopsy, Wide Local Excision or Mastectomy with or without axillary lymph node dissection specimen and matched immunohistochemistry reports were merged into a single file and analysed with SPSS v. 20.0. Descriptive statistics of frequencies and percentages were used to describe categorical variables. Cross-tabulation and chi-square test was done at a 95% confidence interval with significance established at p < 0.05. RESULTS: A total of 2418 cases were included in the study with 20.2% (488 cases) being EOBCs and 79.8% (1930 cases) being LOBCs. The median age at diagnosis was 34.66 (IQR: 5.55) in the EOBC group (< 40 years) and 54.29 (IQR: 16.86) in the LOBC group (≥ 40 years). Invasive carcinoma-No Special Type was the commonest tumour type with grade III tumours being the commonest in both categories of patients. Perineural invasion was the only statistically significant pathologic parameter with age. EOBC was associated with higher DCIS component (24.8% vs 21.6%), lower hormone-receptor-positive status (52.30% vs 55.70%), higher proliferation index (Ki-67 > 20: 82.40% vs 80.30%) and a higher number of involved lymph nodes (13.80% vs 9.00%). Triple-Negative Breast cancer (26.40% vs 24.30%) was the most predominant molecular subtype of EOBC. CONCLUSION: EOBCs in our setting are generally more aggressive with poorer prognostic histopathological and molecular features when compared with LOBCs. A larger study is recommended to identify the association between relevant pathological features and early onset breast cancer in Ghana. Again, further molecular and genetic studies to understand the molecular genetic drivers of the general poorer pathological features of EOBCs and its relation to patient outcome in our setting is needed.

Assessing the knowledge and practices of occupational safety and health in the artisanal and small-scale gold mining sector of Ghana: A case of obuasi.

It is noted that most developing countries such as Ghana are still grappling with health and safety policies and practices as governments and corporations have made insignificant efforts. This study utilized a questionnaire since it is suitable for both illiterate and literate respondents and allows for the gathering of massive quantities of data in a short space of time. The descriptive statistics, chi-square, likelihood ratio test regression, and correlation were used to assess the occupational safety and health knowledge and practices of employees in the artisanal small-scale gold mine in Obuasi. Although majority of the respondents had less than 5 years of job experience, however about 36% and 19% of the respondents had between 4-10 years and 11-15 years of experience respectively. However, the study further observed that an increase in health and safety practices such as safety training, committee, education, facilities, etc. will increase the level of knowledge of respondents on health and safety policies. The study further revealed that the more health and safety practices, the higher the primary responsibility of site managers to ensure that workers are practicing safety. However, the general working conditions of respondents did not meet acceptable standards as revealed by the observation checklist. The study, therefore, recommends that prime Stakeholders in the ASGM need to consider investing in future research on ASGM safety issues to acquire accurate database on ASGM operations safety.

Characterisation of urban environment and activity across space and time using street images and deep learning in Accra.

The urban environment influences human health, safety and wellbeing. Cities in Africa are growing faster than other regions but have limited data to guide urban planning and policies. Our aim was to use smart sensing and analytics to characterise the spatial patterns and temporal dynamics of features of the urban environment relevant for health, liveability, safety and sustainability. We collected a novel dataset of 2.1 million time-lapsed day and night images at 145 representative locations throughout the Metropolis of Accra, Ghana. We manually labelled a subset of 1,250 images for 20 contextually relevant objects and used transfer learning with data augmentation to retrain a convolutional neural network to detect them in the remaining images. We identified 23.5 million instances of these objects including 9.66 million instances of persons (41% of all objects), followed by cars (4.19 million, 18%), umbrellas (3.00 million, 13%), and informally operated minibuses known as tro tros (2.94 million, 13%). People, large vehicles and market-related objects were most common in the commercial core and densely populated informal neighbourhoods, while refuse and animals were most observed in the peripheries. The daily variability of objects was smallest in densely populated settlements and largest in the commercial centre. Our novel data and methodology shows that smart sensing and analytics can inform planning and policy decisions for making cities more liveable, equitable, sustainable and healthy.

Between Policy and Risk Communication: Coverage of Air Pollution in Ghanaian Newspapers.

Mass media plays an increasingly persuasive role in orienting political decisions, shaping social agendas, influencing individuals' actions, and interpreting scientific evidence for the public. With growing scientific understanding of the health, social and environmental consequences of air pollution, there is an urgent need to understand how media coverage frames these links, particularly in Low- and Middle-Income Countries. This paper examines how the Ghanaian print and electronic media houses are covering air pollution issues given increased efforts at reducing air pollution within the country. The main goal of this work is to track the progress of policies to reduce air pollution. We used a qualitative content analysis of selected newspapers (both traditional and online) between the periods 2016 and 2021 and we found that articles on air pollution have been increasing, with more reportage on impact and policy issues compared to causes of air pollution. A focus group with six members of the media confirmed an interest in covering health and environmental issues, particularly coverage of specific diseases and human-interest pieces. This increasing attention is likely associated with intensifying local, national, and international action to improve air quality in Ghana, and growing awareness of the health impacts of air pollution.

Spatial heterogeneity in drinking water sources in the Greater Accra Metropolitan Area (GAMA), Ghana.

Universal access to safe drinking water is essential to population health and well-being, as recognized in the Sustainable Development Goals (SDG). To develop targeted policies which improve urban access to improved water and ensure equity, there is the need to understand the spatial heterogeneity in drinking water sources and the factors underlying these patterns. Using the Shannon Entropy Index and the Index of Concentration at the Extremes at the enumeration area level, we analyzed census data to examine the spatial heterogeneity in drinking water sources and neighborhood income in the Greater Accra Metropolitan Area (GAMA), the largest urban agglomeration in Ghana. GAMA has been a laboratory for studying urban growth, economic security, and other concomitant socio-environmental and demographic issues in the recent past. The current study adds to this literature by telling a different story about the spatial heterogeneity of GAMA's water landscape at the enumeration area level. The findings of the study reveal considerable geographical heterogeneity and inequality in drinking water sources not evidenced in previous studies. We conclude that heterogeneity is neither good nor bad in GAMA judging by the dominance of both piped water sources and sachet water (machine-sealed 500-ml plastic bag of drinking water). The lessons from this study can be used to inform the planning of appropriate localized solutions targeted at providing piped water sources in neighborhoods lacking these services and to monitor progress in achieving universal access to improved drinking water as recognized in the SDG 6 and improving population health and well-being.

Spatial modelling and inequalities of environmental noise in Accra, Ghana.

Noise pollution is a growing environmental health concern in rapidly urbanizing sub-Saharan African (SSA) cities. However, limited city-wide data constitutes a major barrier to investigating health impacts as well as implementing environmental policy in this growing population. As such, in this first of its kind study in West Africa, we measured, modelled and predicted environmental noise across the Greater Accra Metropolitan Area (GAMA) in Ghana, and evaluated inequalities in exposures by socioeconomic factors. Specifically, we measured environmental noise at 146 locations with weekly (n = 136 locations) and yearlong monitoring (n = 10 locations). We combined these data with geospatial and meteorological predictor variables to develop high-resolution land use regression (LUR) models to predict annual average noise levels (LAeq24hr, Lden, Lday, Lnight). The final LUR models were selected with a forward stepwise procedure and performance was evaluated with cross-validation. We spatially joined model predictions with national census data to estimate population levels of, and potential socioeconomic inequalities in, noise levels at the census enumeration-area level. Variables representing road-traffic and vegetation explained the most variation in noise levels at each site. Predicted day-evening-night (Lden) noise levels were highest in the city-center (Accra Metropolis) (median: 64.0 dBA) and near major roads (median: 68.5 dBA). In the Accra Metropolis, almost the entire population lived in areas where predicted Lden and night-time noise (Lnight) surpassed World Health Organization guidelines for road-traffic noise (Lden <53; and Lnight <45). The poorest areas in Accra also had significantly higher median Lden and Lnight compared with the wealthiest ones, with a difference of ∼5 dBA. The models can support environmental epidemiological studies, burden of disease assessments, and policies and interventions that address underlying causes of noise exposure inequalities within Accra.

The distribution of covert microbial natural enemies of a globally invasive crop pest, fall armyworm, in Africa: Enemy release and spillover events.

Invasive species pose a significant threat to biodiversity and agriculture world-wide. Natural enemies play an important part in controlling pest populations, yet we understand very little about the presence and prevalence of natural enemies during the early invasion stages. Microbial natural enemies of fall armyworm Spodoptera frugiperda are known in its native region, however, they have not yet been identified in Africa where fall armyworm has been an invasive crop pest since 2016. Larval samples were screened from Malawi, Rwanda, Kenya, Zambia, Sudan and Ghana for the presence of four different microbial natural enemies; two nucleopolyhedroviruses, Spodoptera frugiperda NPV (SfMNPV) and Spodoptera exempta NPV (SpexNPV); the fungal pathogen Metarhizium rileyi; and the bacterium Wolbachia. This study aimed to identify which microbial pathogens are present in invasive fall armyworm, and determine the geographical, meteorological and temporal variables that influence prevalence. Within 3 years of arrival, fall armyworm was exposed to all four microbial natural enemies. SfMNPV probably arrived with fall armyworm from the Americas, but this is the first putative evidence of host spillover from Spodoptera exempta (African armyworm) to fall armyworm for the endemic pathogen SpexNPV and for Wolbachia. It is also the first confirmed incidence of M. rileyi infecting fall armyworm in Africa. Natural enemies were localised, with variation being observed both nationally and temporally. The prevalence of SfMNPV (the most common natural enemy) was predominantly explained by variables associated with the weather; declining with increasing rainfall and increasing with temperature. However, virus prevalence also increased as the growing season progressed. The infection of an invasive species with a natural enemy from its native range and novel pathogens specific to its new range has important consequences for understanding the population ecology of invasive species and insect-pathogen interactions. Additionally, while it is widely known that temporal and geographic factors affect insect populations, this study reveals that these are important in understanding the distribution of microbial natural enemies associated with invasive pests during the early stages of invasion, and provide baseline data for future studies.