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Heliyon ; 4(11): e00931, 2018 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-30480156


The realization of the scale, magnitude, and complexity of the water and sanitation problem at the global level has compelled international agencies and national governments to increase their resolve to face the challenge. There is extensive evidence on the independent effects of urbanicity (rural-urban environment) and wealth status on access to water and sanitation services in sub-Saharan Africa. However, our understanding of the joint effect of urbanicity and wealth on access to water and sanitation services across spatio-temporal scales is nascent. In this study, a pooled regression analysis of the compositional and contextual factors that systematically vary with access to water and sanitation services over a 25-year time period in fifteen countries across sub-Saharan Africa (SSA) was carried out. On the whole, substantial improvements have been made in providing access to improved water sources in SSA from 1990 to 2015 unlike access to sanitation facilities over the same period. Households were 28.2 percent and 125.2 percent more likely to have access to improved water sources in 2000-2005 and 2010-2015 respectively, than in 1990-1995. Urban rich households were 329 percent more likely to have access to improved water sources compared with the urban poor. Although access to improved sanitation facilities increased from 69 percent in 1990-1995 and 74 percent in 2000-2005 it declined significantly to 53 percent in 2010-2015. Urban rich households were 227 percent more likely to have access to improved sanitation facilities compared with urban poor households. These results were mediated and attenuated by biosocial, socio-cultural and contextual factors and underscore the fact that the challenge of access to water and sanitation in sub-Saharan Africa is not merely scientific and technical but interwoven with environment, culture, economics and human behaviour necessitating the need for interdisciplinary research and policy interventions.

J Water Health ; 15(4): 658-672, 2017 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-28771162


Chemometric techniques were applied to evaluate the spatial and temporal heterogeneities in groundwater quality data for approximately 740 goldmining and agriculture-intensive locations in Ghana. The strongest linear and monotonic relationships occurred between Mn and Fe. Sixty-nine per cent of total variance in the dataset was explained by four variance factors: physicochemical properties, bacteriological quality, natural geologic attributes and anthropogenic factors (artisanal goldmining). There was evidence of significant differences in means of all trace metals and physicochemical parameters (p < 0.001) between goldmining and non-goldmining locations. Arsenic and turbidity produced very high value F's demonstrating that 'physical properties and chalcophilic elements' was the function that most discriminated between non-goldmining and goldmining locations. Variations in Escherichia coli and total coliforms were observed between the dry and wet seasons. The overall predictive accuracy of the discriminant function showed that non-goldmining locations were classified with slightly better accuracy (89%) than goldmining areas (69.6%). There were significant differences between the underlying distributions of Cd, Mn and Pb in the wet and dry seasons. This study emphasizes the practicality of chemometrics in the assessment and elucidation of complex water quality datasets to promote effective management of groundwater resources for sustaining human health.

Monitoramento Ambiental/métodos , Água Subterrânea/química , Metais/análise , Oligoelementos/análise , Poluição Química da Água/análise , Qualidade da Água , Gana , Modelos Teóricos
Ecohealth ; 12(2): 227-43, 2015 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-24136388


Most countries in Sub-Saharan Africa including Ghana still depend extensively on unprocessed solid cooking fuels with many people exposed on a daily basis to harmful emissions and other health risks. In this study, using complementary log-log multivariate models, we estimated the health effects of exposure to smoke from unprocessed wood in four regions of Ghana while controlling for socio-environmental and socio-demographic factors. The results show that the distribution of self-reported exposure to smoke was highest among participants in the Northern region, rural dwellers, the 25-49 age groups, individuals with no education, and married women. As expected, exposure to smoke was higher in crowded households and in communities without basic social amenities. Region, residential locality, housing quality (type of roofing, floor and exterior materials), self-reported housing condition, and access to toilet facilities were associated with self-reported exposure to solid fuel smoke. Participants living in urban areas were less likely (OR = 0.82, ρ ≤ 0.01) to be exposed to solid fuel smoke compared to their rural counterparts. An inverse relationship between self-reported housing condition and exposure to solid fuel smoke was observed and persisted even after adjustments were made for confounding variables in the demographic model. In Ghana, the cost and intermittent shortages of liquefied petroleum gas and other alternative fuel sources hold implications for the willingness of the poor to shift to their use. Thus, the poorest rural populations with nearly no cash income and electricity, but with access to wood and/or agricultural waste, are unlikely to move to clean fuels or use significantly improved stoves without large subsidies, which are usually not sustainable. However, there appears to be large populations between these extremes that can be targeted by efforts to introduce improved stoves.

Poluentes Atmosféricos/análise , Poluição do Ar em Ambientes Fechados/análise , Culinária/métodos , Habitação/normas , Fumaça/análise , Adolescente , Adulto , Idoso , Feminino , Gana , Humanos , Masculino , Pessoa de Meia-Idade , Características de Residência , Autorrelato , Fatores Sexuais , Fatores Socioeconômicos , Adulto Jovem
Artigo em Inglês | MEDLINE | ID: mdl-20924925


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 Hawth's 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.

Metais Pesados/análise , Mineração , Poluentes Químicos da Água/análise , Monitoramento Ambiental , Gana , Análise Multivariada