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At least 19 records
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NASA NTRS2021

The Large Footprint of Small-scale Artisanal Gold Mining in Ghana

Gold mining has played a significant role in Ghana's economy for centuries. Regulation of this industry has varied over time and while industrial mining is prevalent in the country, the expansion of artisanal mining, or Galamsey has escalated in recent years. Many of these artisanal mines are not only harmful to human health due to the use of Mercury (Hg) in the amalgamation process, but also leave a significant footprint on terrestrial ecosystems, degrading and destroying forested ecosystems in the region. In this study, the Landsat image archive available through Google Earth Engine was used to quantify the total footprint of vegetation loss due to artisanal goldmines in Ghana from 2005 to 2019 and understand how conversion of forested regions to mining has changed over a decadal period from 2007 to 2017. A combination of machine learning and change detection algorithms were used to calculate different land cover conversions and the timing of conversion annually. Within the study area of southwestern Ghana, our results indicate that approximately 47,000 ha (⨦2218 ha) of vegetation were converted to mining at an average rate of ~2600 ha yr−1. The results indicate that a high percentage(~50%) of this mining occurred between 2014 and 2017. Around 700 ha of this mining occurred within protected areas as mapped by the World Database of Protected Areas. In addition to deforestation, increased artisanal mining activity in recent years has the potential to affect human health, access to drinking water resources and food security. This work expands upon limited research into the spatial footprint of Galamseyin Ghana, complements mapping efforts by local geographers, and will support efforts by the government of Ghana to monitor deforestation caused by artisanal mining.

Abigail Barenblitt↗
DOE OSTI2025

Implications of Changes in Water Stress and Precipitation Extremes for Cocoa Production in Côte D'ivoire and Ghana

ABSTRACT Climate change induces high variability in drought patterns and extreme precipitation indices in rainfed cocoa farming, impacting cocoa production. This study evaluated water stress, meteorological and agricultural drought conditions, and critical extreme precipitation indices in the world's two largest cocoa‐producing nations from 1981 to 2022. The results revealed a significant reduction in total annual precipitation (PRCPTOT), in the last three decades, with the greatest decline in the 1991–2000 and 2011–2022 periods. Ghana experienced the most significant reduction up to 15% (200 mm/year) in the last decade, attributed to a substantial decrease in wet days number (RR1) up to 25 days per year, a reduction in maximum consecutive wet days (CWD) up to 6 days per year, and an increase in maximum consecutive dry days (CDD) up to 15 days per year. Moreover, there was a notable decline in the Simple Daily Intensity Index (SDII), with reductions of up to 4 mm/day in certain areas, contributing to increased drought frequency, severity, and duration. In the most recent decade (2011–2022), particularly during the extremely dry years of 2013 and 2015, cocoa‐growing regions in Ghana (GHA) and eastern Côte d'Ivoire (CIV) experienced prolonged agricultural drought expressed by soil moisture deficit, typically extending from May to September. Additionally, large portions of central and eastern Ghana, as well as northeastern Côte d'Ivoire experienced sustained water stress, with over three consecutive months of total monthly precipitation falling below 100 mm, negatively impacting cocoa productivity. The decrease in the yield in the range of 2.5% to 37% was noted in the dry years and the following years, varying according to the country depending on the severity of the drought. Sensitivity analysis highlights cocoa yield's responsiveness to drought and water stress, particularly in specific years when water stress occurred, such as 1984,1985, 1989, 1995, 1999, 2000, and 2008. Considering the observed trends in precipitation patterns and their impact on cocoa production, it is crucial to acknowledge the inherent uncertainty of future precipitation patterns due to climate change. To address this challenge effectively, our study underscores the importance of identifying and closely monitoring regions currently facing water stress, as determined by precipitation and drought indicators. Over the analysed period (1981–2022), we have noted shifts in the distribution of water‐stressed areas, highlighting the dynamic nature of this issue. Consequently, we advocate for a targeted approach to implement cocoa supplementary irrigation in consistently water‐stressed regions.

Obahoundje, Salomon [International Water Managemen↗
NASA NTRS2021

Assessment of aerosol burden over Ghana

Although air pollution in Ghana is ranked number one in environmental health threats to public health and sixth to cause of deaths, routine monitoring is rare. This paper presents fourteen years (2005-2018) assessment of aerosol optical depth (AOD) at 3 km resolution from MODIS Aqua and Terra satellites to ascertain the Spatio-temporal and seasonal distribution of aerosols over Ghana and its major cities. The MODIS AOD at 3 km were validated against ground-based Aerosol Robotic Network (AERONET) AODs to ascertain the suitability of the MODIS 3 km data for air quality application in the region. The contribution of distant aerosols to city aerosol loadings was also assessed with Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) backscatter model. A moderate-high aerosol burden (AODs ~ 0.50) was observed over Ghana with a significant contribution from the pre-monsoon season. City centres of Takoradi and Kumasi showed higher aerosol loads (AODs ~ 0.80) than Accra and Tamale. The HYSPLIT model showed that distant or transported aerosol sources to the city centres were of both marine and land generated origins. Linear regression analysis between MODIS AOD and AERONET AOD showed a reasonably good correlation of ~ 0.60 for Aqua and Terra. From the validation analysis, both Aqua and Terra satellites can be used for air quality monitoring over Ghana; however, more ground research must be conducted to ascertain better aerosol model assumptions for the region.

Aerosols↗
DOE OSTI2024

Urbanization and malaria have a contextual relationship in endemic areas: A temporal and spatial study in Ghana

In West Africa, malaria is one of the leading causes of disease-induced deaths. Existing studies indicate that as urbanization increases, there is corresponding decrease in malaria prevalence. However, in malaria-endemic areas, the prevalence in some rural areas is sometimes lower than in some peri-urban and urban areas. Therefore, the relationship between the degree of urbanization, the impact of living in urban areas, and the prevalence of malaria remains unclear. This study explores this association in Ghana, using epidemiological data at the district level (2015–2018) and data on health, hygiene, and education. We applied a multilevel model and time series decomposition to understand the epidemiological pattern of malaria in Ghana. Then we classified the districts of Ghana into rural, peri-urban, and urban areas using administratively defined urbanization, total built areas, and built intensity. We converted the prevalence time series into cross-sectional data for each district by extracting features from the data. To predict the determinant most impacting according to the degree of urbanization, we used a cluster-specific random forest. We find that prevalence is impacted by seasonality, but the trend of the seasonal signature is not noticeable in urban and peri-urban areas. While urban districts have a slightly lower prevalence, there are still pockets with higher rates within these regions. These areas of high prevalence are linked to proximity to water bodies and waterways, but the rise in these same variables is not associated with the increase of prevalence in peri-urban areas. The increase in nightlight reflectance in rural areas is associated with an increased prevalence. We conclude that urbanization is not the main factor driving the decline in malaria. However, the data indicate that understanding and managing malaria prevalence in urbanization will necessitate a focus on these contextual factors. Finally, we design an interactive tool, ’malDecision’ that allows data-supported decision-making.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Molecular Epidemiology of HIV-1 in Ghana: Subtype Distribution, Drug Resistance and Coreceptor Usage

The greatest HIV-1 genetic diversity is found in West/Central Africa due to the pandemic’s origins in this region, but this diversity remains understudied. We characterized HIV-1 subtype diversity (from both sub-genomic and full-genome viral sequences), drug resistance and coreceptor usage in 103 predominantly (90%) antiretroviral-naive individuals living with HIV-1 in Ghana. Full-genome HIV-1 subtyping confirmed the circulating recombinant form CRF02_AG as the dominant (53.9%) subtype in the region, with the complex recombinant 06_cpx (4%) present as well. Unique recombinants, most of which were mosaics containing CRF02_AG and/or 06_cpx, made up 37% of sequences, while “pure” subtypes were rare (<6%). Pretreatment resistance to at least one drug class was observed in 17% of the cohort, with NNRTI resistance being the most common (12%) and INSTI resistance being relatively rare (2%). CXCR4-using HIV-1 sequences were identified in 23% of participants. Overall, our findings advance our understanding of HIV-1 molecular epidemiology in Ghana. Extensive HIV-1 genetic diversity in the region appears to be fueling the ongoing creation of novel recombinants, the majority CRF02_AG-containing, in the region. The relatively high prevalence of pretreatment NNRTI resistance but low prevalence of INSTI resistance supports the use of INSTI-based first-line regimens in Ghana.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

The 2022 Outbreaks of African Swine Fever Virus Demonstrate the First Report of Genotype II in Ghana

African swine fever (ASF) is a lethal disease of domestic pigs that has been causing outbreaks for over a century in Africa ever since its first discovery in 1921. Since 1957, there have been sporadic outbreaks outside of Africa; however, no outbreak has been as devastating and as far-reaching as the current pandemic that originated from a 2007 outbreak in the Republic of Georgia. Derivatives with a high degree of similarity to the progenitor strain, ASFV-Georgia/2007, have been sequenced from various countries in Europe and Asia. However, the current strains circulating in Africa are largely unknown, and 24 different genotypes have been implicated in different outbreaks. In this study, ASF isolates were collected from samples from swine suspected of dying from ASF on farms in Ghana in early 2022. While previous studies determined that the circulating strains in Ghana were p72 Genotype I, we demonstrate here that the strains circulating in 2022 were derivatives of the p72 Genotype II pandemic strain. Therefore, this study demonstrates for the first time the emergence of Genotype II ASFV in Ghana.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2024

Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM2.5 Monitoring in Accra, Ghana

Particulate matter air pollution is a leading cause of global mortality, particularly in Asia and Africa. Addressing the high and wide-ranging air pollution levels requires ambient monitoring, but many low- and middle-income countries (LMICs) remain scarcely monitored. To address these data gaps, recent studies have utilized low-cost sensors. These sensors have varied performance, and little literature exists about sensor intercomparison in Africa. By colocating 2 QuantAQ Modulair-PM, 2 PurpleAir PA-II SD, and 16 Clarity Node-S Generation II monitors with a reference-grade Teledyne monitor in Accra, Ghana, we present the first intercomparisons of different brands of low-cost sensors in Africa, demonstrating that each type of low-cost sensor PM2.5 is strongly correlated with reference PM2.5, but biased high for ambient mixture of sources found in Accra. When compared to a reference monitor, the QuantAQ Modulair-PM has the lowest mean absolute error at 3.04 μg/m3, followed by PurpleAir PA-II (4.54 μg/m3) and Clarity Node-S (13.68 μg/m3). We also compare the usage of 4 statistical or machine learning models (Multiple Linear Regression, Random Forest, Gaussian Mixture Regression, and XGBoost) to correct low-cost sensors data, and find that XGBoost performs the best in testing (R2: 0.97, 0.94, 0.96; mean absolute error: 0.56, 0.80, and 0.68 μg/m3 for PurpleAir PA-II, Clarity Node-S, and Modulair-PM, respectively), but tree-based models do not perform well when correcting data outside the range of the colocation training. Therefore, we used Gaussian Mixture Regression to correct data from the network of 17 Clarity Node-S monitors deployed around Accra, Ghana, from 2018 to 2021. We find that the network daily average PM2.5 concentration in Accra is 23.4 μg/m3, which is 1.6 times the World Health Organization Daily PM2.5 guideline of 15 μg/m3. While this level is lower than those seen in some larger African cities (such as Kinshasa, Democratic Republic of the Congo), mitigation strategies should be developed soon to prevent further impairment to air quality as Accra, and Ghana as a whole, rapidly grow.

Humidity↗
DOE OSTI2024

A Retrospective Analysis Reveals That the 2021 Outbreaks of African Swine Fever Virus in Ghana Were Caused by Two Distinct Genotypes

African swine fever virus (ASFV) is the causative agent of African swine fever (ASF), a highly infectious and lethal disease of domesticated swine. Outbreaks of ASF have been mostly restricted to the continent of Africa. The outbreaks that have occurred outside of Africa were controlled by extensive depopulation of the domesticated pig population. However, in 2007, an outbreak occurred in the country of Georgia, where ASFV infected wild pigs and quickly spread across eastern Europe. Since the reintroduction of ASF into Europe, variants of the current pandemic strain, ASFV Georgia 2007/01 (ASFV-G), which is classified as Genotype 2 based on p72 sequencing, have been reported in countries within western Europe, Asia, and the island of Hispaniola. Additionally, isolates collected in 2020 confirmed the presence of variants of ASFV-G in Nigeria. Recently, we reported similar variants of ASFV-G collected from domestic pigs suspected of dying of ASF in Ghana in 2022. Here, we retroactively report, based on full-length sequencing, that similar variants were present in Ghana in 2021. The SNP analysis revealed derivatives of ASFV with distinct genetic markers. Furthermore, we identified three full-length ASFV genomes as Genotype 1, indicating that there were two genotypes circulating in proximity during the 2021 ASF outbreaks in Ghana.

Virology↗
DOE OSTI2026

Heat exposure and maternal stress: evidence from the GRAPHS pregnancy cohort in Ghana

Heat exposure has been linked to psychosocial stress, an established antecedent of perinatal depression; however, evidence on heat-related stress during pregnancy in sub-Saharan Africa remains limited. We analyzed psychosocial stress scores and covariate data from the Ghana Randomized Air Pollution and Health Study, linking daily maximum and minimum shaded wet bulb globe temperature (WBGT) metrics to participants’ stress scores derived from the Crisis in Family Systems-Revised Life Events Questionnaire. We evaluated associations using ordinal logistic regression of pregnancy-average and trimester-average exposures and distributed lag non-linear models (DLNMs) to assess time-varying associations across gestation. Higher average maximum WBGT exposure across pregnancy was associated with increased odds of higher psychosocial stress; each 1 °C increase in maximum WBGT was associated with 64% higher odds of belonging to a higher stress category (OR = 1.64; 95% CI = 1.17–2.31; p = 0.0040). In trimester-average models, higher first-trimester maximum WBGT was also associated with higher stress (OR = 1.44; 95% CI = 1.15–1.81; p = 0.0014). DLNMs suggested that relatively cooler daily maximum WBGT values (25th percentile) were associated with decreased odds of stress in early pregnancy, whereas extreme daily maximum WBGT values (99th percentile) showed a pattern consistent with increased odds of stress in mid-to-late gestation. These findings highlight gestational windows in which heat exposure may influence stress, emphasizing the need for further research into underlying mechanisms and effective interventions to protect maternal mental health in heat-vulnerable settings.

White, Lewis [Columbia University] (ORCID:00090005↗
NASA NTRSDate not supplied

Evaluating the Performance of GPM IMERG Products for Extreme Precipitation Estimation in Volta River Basin of Ghana

The Volta River basin in West Africa, covering a substantial area of Ghana, faces significant water management challenges due to highly variable rainfall and limited availability of high temporal resolution rain gauges. This study, developed under the 2024 Global Precipitation Measurement (GPM) Mission mentorship program, evaluates precipitation in the Volta River basin using high-resolution satellite-derived data from the Integrated Multi-satellite Retrievals for GPM (IMERG). In the absence of sub-daily rainfall data, comparisons were made between daily, monthly, and seasonal precipitation from local gauges and IMERG V06 and V07 (early and final) runs. Results indicated a very weak correlation between daily precipitation estimates from IMERG and gauge data, but a significantly improved correlation for monthly and seasonal estimates. Additionally, IMERG data were used to identify heavy rainfall events and drought periods, which were compared to known occurrences in the region. By addressing these objectives, this study contributes to more accurate extreme precipitation estimation in the Volta River basin, thereby enhancing disaster risk management and climate resilience in Ghana.

Jessica Sutton↗
NASA NTRS2024

Urban Air Quality Management at Low-Cost Using Micro Air Sensors: A Case Study From Accra, Ghana

Urban air quality management is dependent on the availability of local air pollution data. In many major urban centers of Africa, there is limited to non-existent information on air quality. This is gradually changing in part due to the increasing use of micro air sensors which have the potential to enable the generation of ground-based air quality data at fine scales for understanding local emission trends. Regional literature on the application of the high-resolution data for emission source identification in this region is limited. In this study a micro air sensor was co-located at the Physics Department, University of Ghana with a reference grade instrument to evaluate its performance for estimating PM2.5 pollution accurately at fine scales and the value of this data in identification of local sources and their behavior over time. For this study 15 weeks of data at hourly resolution with approximately 2500 data pairs are generated and analyzed (June 01, 2023, to September 15, 2023). For this time period a coefficient of determination (r 2 ) of 0.83 was generated with a mean absolute error (MAE) of 5.44 μgm -3 between the pre local calibration micro air sensor (i.e. out of box) and the reference-grade instrument. Following currently accepted best practice methods (see e.g., PAS4023) a domain specific (i.e. local) calibration factor was generated using a multi-linear regression model and when this factor is applied to the micro air sensor data, a reduction i.e., improvement in MAE to 1.43 μgm -3 was found. Daily variation was calculated, a receptor model was applied, and time series plots as a function of wind direction were generated, including PM2.5/PM10 ratio scatter and count plots to explore the utility of this observational approach for local source identification. The 3 data sets were compared (out of box, domain calibrated and reference-grade) and it was found that although there were variations in the data reported, source areas highlighted based on these data were similar, with input from local sources such as traffic emissions and biomass burning. As the temporal resolution of observational data associated with these micro air sensors is higher than for reference grade instruments (primarily due to costs and logistics limitations), they have the potential to provide insight into the complex, often hyper localized sources associated with urban areas, such as those found in major African cities.

Source apportionment↗
NASA NTRS2021

Application of Gaussian Mixture Regression for the Correction of Low Cost PM2.5 Monitoring Data in Accra, Ghana

Low-cost sensors (LCSs) for air quality monitoring have enormous potential to improve air quality data coverage in resource-limited parts of the world such as sub-Saharan Africa. LCSs, however, are affected by environment and source conditions. To establish high-quality data, LCSs must be collocated and calibrated with reference grade PM2.5 monitors. From March 2020, a low-cost PurpleAir PM2.5 monitor was collocated with a Met One Beta Attenuation Monitor 1020 in Accra, Ghana. While previous studies have shown that multiple linear regression (MLR) and random forest regression (RF) can improve accuracy and correlation between PurpleAir and reference data, MLR and RF yielded suboptimal improvement in the Accra collocation (R2 = 0.81 and R2 = 0.81, respectively). We present the first application of Gaussian mixture regression (GMR) to air quality data calibration and demonstrate improvement over traditional methods by increasing the collocated PM2.5 correlation and accuracy to R2 = 0.88 and MAE = 2.2 μg/cu. m. Gaussian mixture models (GMMs) are a probability density estimator and clustering method from which nonlinear regressions that tolerate missing inputs can be derived. We find that even when given missing inputs, GMR provides better correlation than MLR and RF performed with complete data. GMR also allows us to estimate calibration certainty. When evaluated, 95% confidence intervals agreed with reference PM2.5 data 96% of the time, suggesting that the model accurately assesses its own confidence. Additionally, clustering within the GMM is consistent with climate characteristics, providing confidence that the calibration approach can learn underlying relationships in data.

Sensors↗
NASA NTRS2004

An Ordinary Chondrite Impactor Composition for the Bosumtwi Impact Structure, Ghana, West Africa: Discussion of Siderophile Element Contents and Os and Cr Isotope Data

Osmium isotope data had shown that Ivory Coast tektites contain an extraterrestrial component, but do not allow distinction between chondritic and iron meteorite contamination. PGE abundances of Ivory Coast tektites and impactites and target rocks from the Bosumtwi crater, the source crater of the Ivory Coast tektites, were all relatively high and did not allow to resolve the presence, or identify the nature, of the meteoritic component. However, Cr isotope analyses of an Ivory Coast tektite yielded a distinct 53Cr excess of 0.30+/-0.06, which indicates that the Bosumtwi impactor was an ordinary chondrite.

Koeberl, Christian↗