Ernest K. A. Afrifa

Anthropogenic sources and environmentally relevant concentrations of heavy metals in surface water of a mining district in Ghana: a multivariate statistical approach

The levels of heavy metals in surface water and their potential origin (natural and anthropogenic) were respectively determined and analysed for the Obuasi mining area in Ghana. Using Hawths tool an extension in ArcGIS 9.2 software, a total of 48 water sample points in Obuasi and its environs were randomly selected for study. The magnitude of As, Cu, Mn, Fe, Pb, Hg, Zn and Cd in surface water from the sampling sites were measured by flame Atomic Absorption Spectrophotometry (AAS). Water quality parameters including conductivity, pH, total dissolved solids and turbidity were also evaluated. Principal component analysis and cluster analysis, coupled with correlation coefficient analysis, were used to identify possible sources of these heavy metals. Pearson correlation coefficients among total metal concentrations and selected water properties showed a number of strong associations. The results indicate that apart from tap water, surface water in Obuasi has elevated heavy metal concentrations, especially Hg, Pb, As, Cu and Cd, which are above the Ghana Environmental Protection Agency (GEPA) and World Health Organisation (WHO) permissible levels; clearly demonstrating anthropogenic impact. The mean heavy metal concentrations in surface water divided by the corresponding background values of surface water in Obuasi decrease in the order of Cd > Cu > As > Pb > Hg > Zn > Mn > Fe. The results also showed that Cu, Mn, Cd and Fe are largely responsible for the variations in the data, explaining 72% of total variance; while Pb, As and Hg explain only 18.7% of total variance. Three main sources of these heavy metals were identified. As originates from nature (oxidation of sulphide minerals particularly arsenopyrite-FeAsS). Pb derives from water carrying drainage from towns and mine machinery maintenance yards. Cd, Zn, Fe and Mn mainly emanate from industry sources. Hg mainly originates from artisanal small-scale mining. It cannot be said that the difference in concentration of heavy metals might be attributed to difference in proximity to mining-related activities because this is inconsistent with the cluster analysis. Based on cluster analysis SN32, SN42 and SN43 all belong to group one and are spatially similar. But the maximum Cu concentration was found in SN32 while the minimum Cu concentration was found in SN42 and SN43.

Mapping of noise risk zones derived from religious activities and perceptions in residential neighbourhoods in the Cape Coast metropolis, Ghana

Ambient noise levels emanating from religious activities in residential neighbourhoods are an emerging environmental problem that educes little attention from enforcement agencies and policy makers in Ghana. This paper set out to quantify religious noise exposure in urban residential neighbourhoods in the Cape Coast metropolis of Ghana. Subjective annoyance levels of residents in selected communities were determined. Noise risk zones were mapped using ARCGIS 9.3 software and surface interpolation for the data was carried out using inverse distance weighting. The results show that most (77 and 86 per cent) of the locations recorded noise levels that were above the Ghana Environmental Protection Agency maximum permissible limit for day and night, respectively. Pearsons correlation coefficient for day and night noise exposure shows strong association (0.714) at the 0.01 level. There is variability in the levels of noise for both day and night, which are rather high (standard deviation = 7.59477 and 7.94022, respectively). Generally, levels of noise exposure correlated with levels of annoyance of residents, except that the highest noise exposure was not recorded in the community where the annoyance level of residents was highest. Residential neighbourhoods within the study area largely experienced safe to tolerable levels of religious noise, although 5 per cent were within the high-risk zone. Given that the selected residential areas have high population densities, even when the dispersion of noise risk is spatially limited, it affects a large number of people who belong to different socio-economic classes.