Ghana Meteorological Agency

ABSTRACT While climate-related mobility has been part of life in Africa for centuries, existing literature on migration in the context of climate change generally lacks a historical perspective. This paper historicises climate-related migration in the Northern Savannah Zone of Ghana, with specific focus on the Upper West Region, drawing on climate data, a survey of 403 households, and interviews and focus group discussions with farmers. It demonstrates that migration and mobility in the region are rooted in historical patterns established and reinforced through colonial and post-colonial governance. These patterns reflect inequalities created by past and present development policies as well as environmental factors. While environmental change intensifies existing migration patterns, it is difficult to isolate these effects from the economic, social, and political factors which also contribute to migration flows in the Savannah zone. We therefore conclude that migration flows are co-constituted by past and present governance practices, disparities, development policies, and social transformation as well as environmental factors. Analysis and policy narratives that attribute recent migration flows to climate change only are simplistic and blur the effects of past and present structural inequalities and political engagement.

Air pollution is one of the leading risk factors for poor health in Africa, resulting in millions of premature deaths and economic losses. Of particular interest is exposure to fine particulate matter (PM2.5) which is the driver for a majority of deaths across the continent. However, PM monitoring, and by extension, ground-level data on PM2.5 is very limited; this limits our understanding of the widespread societal and health impacts linked to PM pollution. The robustness of low-cost PM sensors and their ability to report in situ data in tropical environments via internet-based platforms as well as relative affordability has created the opportunity to employ low-cost sensors (LCS) for air quality monitoring but calibration methodologies and the usefulness of the high-temporal resolution data for source identification remain a challenge. Increasingly, local governments in African countries are also turning to low-cost sensors to monitor air quality. In this study, two Airnote PM monitors were colocated with reference-grade Teledyne PM mass monitor T640 for ~4 weeks at the University of Ghana, Accra to establish their performance using a simplified data correction methodology - multiple linear regression (MLR) model. A split ratio of 80% and 20% was used to train and test the populated Airnote PM2.5 data respectively based on measurements from Teledyne T640 with temperature and relative humidity values from the Airnote monitor. Sectoral and calendar analysis with wind component data were used to triangulate the sources of PM2.5. We observed a high consistency between the two Airnote monitors. Hourly and 24-hour average PM2.5 values ranged from 25 to 95 μg/m3, and 29 to 54 μg/m3 respectively, and in most cases, were significantly higher than the WHO Air Quality Guideline. MLR using Pearson’s correlation analysis improved the out-of-the-box quality of low-cost Airnote PM2.5 data; the R2 improved from 0.69 to 0.84 and the mean absolute error from 11.75 to 4.20 μg/m3 respectively. Also, the MLR correction model was found to improve the Airnote PM2.5 data quality for higher relative humidity (between 50 and 90%) but not lower. PM2.5 pollution was local and from N, NE and SW winds for the raw, corrected and Teledyne PM mass monitor T640 measurements. Together, these results indicate that with appropriate corrections, low-cost PM sensors can generate the much needed data for air pollution research and mitigation in areas with limited air quality monitoring and data.

Abstract The univariate statistics of Potassium (K), thorium (Th), and uranium (U) concentrations, in the Earth’s oceanic and continental crust are examined by different techniques. The frequency distributions of the concentrations of these elements in the oceanic crust are derived from a global catalog of mid‐ocean ridge basalts. Their frequency distributions of concentrations in the continental crust are illustrated by the North Pilbara Craton, and the West Africa Craton. For these two cratons, the distributions of K, Th, and U derived from geochemical analyses of several thousand whole rock samples differ significantly from those derived from airborne radiometric surveys. The distributions from airborne surveys tends to be more symmetric with smaller standard deviations than the right‐skewed distributions inferred from whole rock geochemical analyses. Hypothetic causes of these differences include (a) bias in rock sampling or in airborne surveys, (b) the differences between the chemistry of superficial material and rocks, and (c) the differences in scales of measurements. The scale factor, viewed as consequence of the central limit theorem applied to K, Th, and U concentrations, appears to account for most of the observed differences in the distributions of K, Th, and U. It suggests that the three scales of auto‐correlation of K, Th, and U concentrations are of the same order of magnitude as the resolution of the airborne radiometric surveys (50–200 m). Concentrations of K, Th, and U are therefore generally heterogenous at smaller scales.

Knowledge of soil heavy metal concentration is very important for assessing the purity and quality of the soil in an environment. The concentrations of nine heavy metals (NHM), Zn, Pb, Cr, Cu, Co, Ni, Cd, Hg, and As, from the near-surface soils (∼ 0–15 cm) from an industrial cluster in Kumasi, Ghana, were qualitatively and quantitatively measured and analyzed using X-ray fluorescence (XRF) spectroscopy analysis. The sources of these NHM were mainly anthropogenic as a result of the indiscriminate industrial waste disposal. In all, a total of about 100 soil samples were taken from six sampling sites, four of which were industrial and the remaining two residential. Forty soil samples out of the total number were carefully selected for elemental analyses and the mean heavy metal concentrations were calculated using statistical methods. The results from locations of high industrial impact showed that the mean concentrations of the NHM present in the soil were in the order of Zn (189.2−908.6 mgkg−1), Pb (133.7−571.3 mgkg−1), Cr (91.3−545.8 mgkg−1), Cu (62.9−334.6 mgkg−1), Co (38.6−81.9 mgkg−1), Ni (12.4−30.9 mgkg−1), Cd (6.9−13.2 mgkg−1), Hg (5.5−10.4 mg kg−1), and As (2.3−18.6 mgkg−1). Apart from Ni and As, all the heavy metals recorded concentrations that ranged from 10−900% higher than their respective threshold limit values (TLVs). Heavy metal concentrations from the residential sites were comparatively far lower with only Cr, Cd, and Hg registering concentrations between 65−250% above their TLVs. The cluster with its residential communities is at a serious risk of soil heavy metal toxicity and awareness to this needs to be created as such.

International audience